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Mohnen, Sigrid M., Adriënne H. Rotteveel, Gerda Doornbos e Johan J. Polder. "Healthcare Expenditure Prediction with Neighbourhood Variables – A Random Forest Model". Statistics, Politics and Policy 11, n.º 2 (16 de dezembro de 2020): 111–38. http://dx.doi.org/10.1515/spp-2019-0010.
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AbstractWe investigated the additional predictive value of an individual’s neighbourhood (quality and location), and of changes therein on his/her healthcare costs. To this end, we combined several Dutch nationwide data sources from 2003 to 2014, and selected inhabitants who moved in 2010. We used random forest models to predict the area under the curve of the regular healthcare costs of individuals in the years 2011–2014. In our analyses, the quality of the neighbourhood before the move appeared to be quite important in predicting healthcare costs (i.e. importance rank 11 out of 126 socio-demographic and neighbourhood variables; rank 73 out of 261 in the full model with prior expenditure and medication). The predictive performance of the models was evaluated in terms of R2 (or proportion of explained variance) and MAE (mean absolute (prediction) error). The model containing only socio-demographic information improved marginally when neighbourhood was added (R2 +0.8%, MAE −€5). The full model remained the same for the study population (R2 = 48.8%, MAE of €1556) and for subpopulations. These results indicate that only in prediction models in which prior expenditure and utilization cannot or ought not to be used neighbourhood might be an interesting source of information to improve predictive performance.
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Wang, Fangyi, Yongchao Wang, Xiaokang Ji e Zhiping Wang. "Effective Macrosomia Prediction Using Random Forest Algorithm". International Journal of Environmental Research and Public Health 19, n.º 6 (10 de março de 2022): 3245. http://dx.doi.org/10.3390/ijerph19063245.
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(1) Background: Macrosomia is prevalent in China and worldwide. The current method of predicting macrosomia is ultrasonography. We aimed to develop new predictive models for recognizing macrosomia using a random forest model to improve the sensitivity and specificity of macrosomia prediction; (2) Methods: Based on the Shandong Multi-Center Healthcare Big Data Platform, we collected the prenatal examination and delivery data from June 2017 to May 2018 in Jinan, including the macrosomia and normal-weight newborns. We constructed a random forest model and a logistic regression model for predicting macrosomia. We compared the validity and predictive value of these two methods and the traditional method; (3) Results: 405 macrosomia cases and 3855 normal-weight newborns fit the selection criteria and 405 pairs of macrosomia and control cases were brought into the random forest model and logistic regression model. On the basis of the average decrease of the Gini coefficient, the order of influencing factors was: interspinal diameter, transverse outlet, intercristal diameter, sacral external diameter, pre-pregnancy body mass index, age, the number of pregnancies, and the parity. The sensitivity, specificity, and area under curve were 91.7%, 91.7%, and 95.3% for the random forest model, and 56.2%, 82.6%, and 72.0% for logistic regression model, respectively; the sensitivity and specificity were 29.6% and 97.5% for the ultrasound; (4) Conclusions: A random forest model based on the maternal information can be used to predict macrosomia accurately during pregnancy, which provides a scientific basis for developing rapid screening and diagnosis tools for macrosomia.
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Kor, Hakan. "Global solar radiation prediction model with random forest algorithm". Thermal Science 25, Spec. issue 1 (2021): 31–39. http://dx.doi.org/10.2298/tsci200608004k.
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Global solar radiation estimation is crucial for regional climate assessment and crop growth. Therefore, studies on the prediction of solar radiation are emerging. With the availability of the public data on solar radiation, computerized models have been developed as well. These predictive models play significant role in determining the potentials of regions suitable for renewable energy generation required by engineering and agricultural activities. Herein a computerized model has been presented for estimating global solar radiation. The model utilizes random forest algorithm and reached predictive value of 93.9%.
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Rigatti, Steven J. "Random Forest". Journal of Insurance Medicine 47, n.º 1 (1 de janeiro de 2017): 31–39. http://dx.doi.org/10.17849/insm-47-01-31-39.1.
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For the task of analyzing survival data to derive risk factors associated with mortality, physicians, researchers, and biostatisticians have typically relied on certain types of regression techniques, most notably the Cox model. With the advent of more widely distributed computing power, methods which require more complex mathematics have become increasingly common. Particularly in this era of “big data” and machine learning, survival analysis has become methodologically broader. This paper aims to explore one technique known as Random Forest. The Random Forest technique is a regression tree technique which uses bootstrap aggregation and randomization of predictors to achieve a high degree of predictive accuracy. The various input parameters of the random forest are explored. Colon cancer data (n = 66,807) from the SEER database is then used to construct both a Cox model and a random forest model to determine how well the models perform on the same data. Both models perform well, achieving a concordance error rate of approximately 18%.
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Wei, Li-Li, Yue-Shuai Pan, Yan Zhang, Kai Chen, Hao-Yu Wang e Jing-Yuan Wang. "Application of machine learning algorithm for predicting gestational diabetes mellitus in early pregnancy†". Frontiers of Nursing 8, n.º 3 (1 de setembro de 2021): 209–21. http://dx.doi.org/10.2478/fon-2021-0022.
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Abstract Objective To study the application of a machine learning algorithm for predicting gestational diabetes mellitus (GDM) in early pregnancy. Methods This study identified indicators related to GDM through a literature review and expert discussion. Pregnant women who had attended medical institutions for an antenatal examination from November 2017 to August 2018 were selected for analysis, and the collected indicators were retrospectively analyzed. Based on Python, the indicators were classified and modeled using a random forest regression algorithm, and the performance of the prediction model was analyzed. Results We obtained 4806 analyzable data from 1625 pregnant women. Among these, 3265 samples with all 67 indicators were used to establish data set F1; 4806 samples with 38 identical indicators were used to establish data set F2. Each of F1 and F2 was used for training the random forest algorithm. The overall predictive accuracy of the F1 model was 93.10%, area under the receiver operating characteristic curve (AUC) was 0.66, and the predictive accuracy of GDM-positive cases was 37.10%. The corresponding values for the F2 model were 88.70%, 0.87, and 79.44%. The results thus showed that the F2 prediction model performed better than the F1 model. To explore the impact of sacrificial indicators on GDM prediction, the F3 data set was established using 3265 samples (F1) with 38 indicators (F2). After training, the overall predictive accuracy of the F3 model was 91.60%, AUC was 0.58, and the predictive accuracy of positive cases was 15.85%. Conclusions In this study, a model for predicting GDM with several input variables (e.g., physical examination, past history, personal history, family history, and laboratory indicators) was established using a random forest regression algorithm. The trained prediction model exhibited a good performance and is valuable as a reference for predicting GDM in women at an early stage of pregnancy. In addition, there are certain requirements for the proportions of negative and positive cases in sample data sets when the random forest algorithm is applied to the early prediction of GDM.
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Diaz, Pablo, Juan C. Salas, Aldo Cipriano e Felipe Núñez. "Random forest model predictive control for paste thickening". Minerals Engineering 163 (março de 2021): 106760. http://dx.doi.org/10.1016/j.mineng.2020.106760.
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Mao, Yiwen, e Asgeir Sorteberg. "Improving Radar-Based Precipitation Nowcasts with Machine Learning Using an Approach Based on Random Forest". Weather and Forecasting 35, n.º 6 (dezembro de 2020): 2461–78. http://dx.doi.org/10.1175/waf-d-20-0080.1.
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AbstractA binary classification model is trained by random forest using data from 41 stations in Norway to predict the precipitation in a given hour. The predictors consist of results from radar nowcasts and numerical weather predictions. The results demonstrate that the random forest model can improve the precipitation predictions by the radar nowcasts and the numerical weather predictions. This study clarifies whether certain potential factors related to model training can influence the predictive skill of the random forest method. The results indicate that enforcing a balanced prediction by resampling the training datasets or lowering the threshold probability for classification cannot improve the predictive skill of the random forest model. The study reveals that the predictive skill of the random forest model shows seasonality, but is only weakly influenced by the geographic diversity of the training dataset. Finally, the study shows that the most important predictor is the precipitation predictions by the radar nowcasts followed by the precipitation predictions by the numerical weather predictions. Although meteorological variables other than precipitation are weaker predictors, the results suggest that they can help to reduce the false alarm ratio and to increase the success ratio of the precipitation prediction.
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Bashir Suleiman, Aminu, Stephen Luka e Muhammad Ibrahim. "CARDIOVASCULAR DISEASE PREDICTION USING RANDOM FOREST MACHINE LEARNING ALGORITHM". FUDMA JOURNAL OF SCIENCES 7, n.º 6 (31 de dezembro de 2023): 282–89. http://dx.doi.org/10.33003/fjs-2023-0706-2128.
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Every year, cardiovascular disease (CVD) claims the lives of nearly 17 million people worldwide. Predicting heart disease early and accurately can help delay therapies and improve results. Patient data analysis machine learning techniques have shown promise for better predictive capabilities than conventional methods; however, there are still gaps in areas such as algorithm blending, standardization, feature optimization, and model tuning that require strong methodology. By benchmarking against established methods, this study attempts to create a more sophisticated machine learning model with detailed performance and a robust approach for predicting heart disease. Using a clinical dataset that was obtained from an internet repository, an improved random forest (RF) model was created. It was then tested against baseline logistic regression and support vector machine models, Naïve Bayes Classifier, K Nearest Neighbors Classifier, and Decision Tree Classifier. RF hyperparameter tweaking, redundant feature filtering, and systematic data preprocessing were used. Accuracy, precision, recall, F1 score, and ROC analysis were computed as evaluation measures. With F1 score, 1.00 AUC, and 90% accuracy, The RF model demonstrated superior performance compared to the remaining models, which exhibited, AUCs of 0.9, 0.82, and 0.9. On the public dataset, the refined RF model demonstrated exceptional predictive performance, highlighting the promise of a methodical machine learning approach to improve heart disease prediction. The external clinical validation and optimization of various patient populations should be the main areas of attention for future research.
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Jeong, Hoyeon, Youngjune Kim e So Yeong Lim. "A Predictive Model for Farmland Purchase/Rent Using Random Forests". Korean Agricultural Economics Association 63, n.º 3 (30 de setembro de 2022): 153–68. http://dx.doi.org/10.24997/kjae.2022.63.3.153.
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This study contributes to guidance for understanding farmland purchase and rent decisions in Korea via an analysis using a machine learning tool, Random Forests: A Supervised Machine Learning Algorithm. Farm Household Economy Survey is employed to predict the relationship between farmland acquisition and farm household economic characteristics. Our main findings are two folds. First, a farmland purchase decision is positively related to transfer incomes, the value of inventory & fixed assets, and the value of farmland that farmers owned. Second, a farmland rent decision is also positively associated with a rent paid in a prior year, revenue from field crops, inventory and agricultural assets, and transfer incomes.
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Emir, Senol, Hasan Dincer, Umit Hacioglu e Serhat Yuksel. "Random Regression Forest Model using Technical Analysis Variables". International Journal of Finance & Banking Studies (2147-4486) 5, n.º 3 (21 de julho de 2016): 85–102. http://dx.doi.org/10.20525/ijfbs.v5i3.461.
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The purpose of this study is to explore the importance and ranking of technical analysis variables in Turkish banking sector. Random Forest method is used for determining importance scores of inputs for eight banks in Borsa Istanbul. Then two predictive models utilizing Random Forest (RF) and Artificial Neural Networks (ANN) are built for predicting BIST-100 index and bank closing prices. Results of the models are compared by three metrics namely Mean Absolute Error (MAE), Mean Square Error (MSE), Median Absolute Error (MedAE). Findings show that moving average (MAV-100) is the most important variable for both BIST -100 index and bank closing prices. Therefore, investors should follow this technical indicator with respect to Turkish banks. In addition ANN shows better performance for all metrics.
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Nie, Ying, e Yundong Xu. "Prediction On Tiktok Like Behavior Based on Random Forest Model". Highlights in Science, Engineering and Technology 101 (20 de maio de 2024): 292–98. http://dx.doi.org/10.54097/d6metn07.
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In recent years, the TikTok short video platform has rapidly ascended, attracting a plethora of users to participate in content creation and interaction. Predicting 'like' behavior delves deeply into user preferences, offering reference value for the platform to enhance traffic. Based on this, the present paper focuses on TikTok 'like' behavior as the research subject and employs a Random Forest model for its prediction. The model's fit was enhanced by optimizing the number of estimators (n_e) and the maximum number of features considered for splitting a node (max_f), aiming to provide a beneficial reference for TikTok and other social media platforms atop optimizing existing research. The results demonstrate that the fitted model boasts a commendable predictive performance, with an accuracy of 99.07%. The application of the model will aid the TikTok short video platform and other platforms in making informed video recommendations to users, thus improving the user experience.
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Ren, Keying. "House Price Prediction Based on Machine Learning Algorithms - Taking Ames as an Example". Advances in Economics, Management and Political Sciences 85, n.º 1 (28 de maio de 2024): 181–89. http://dx.doi.org/10.54254/2754-1169/85/20240870.
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This study delves into the significance and methods of predicting housing prices. Utilizing a dataset from Kaggle, the author selected 10 variables highly correlated with housing prices, including OverallQual, GrLivArea, and GarageCars. Various models such as random forest and multiple linear regression were employed for prediction and comparison. Results indicate that for data with strong linear relationships, the predictive performance of the multiple linear regression model surpasses that of the random forest model. The paper emphasizes the importance of data preprocessing on model accuracy and suggests that model selection should align with data characteristics and problem requirements. While providing a preliminary exploration of housing price prediction, the study acknowledges shortcomings such as incomplete variable selection and insufficient data processing, suggesting avenues for future research to address these limitations and enhance the predictive capabilities in this domain.
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Mathew, Dr Tina Elizabeth. "An Improvised Random Forest Model for Breast Cancer Classification". NeuroQuantology 20, n.º 5 (18 de maio de 2022): 713–22. http://dx.doi.org/10.14704/nq.2022.20.5.nq22227.
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Breast Cancer is considered as the most common cancer in females with high incidence rate. The evolution of modern facilities has helped in reducing the mortality rate, yet the incidence is still the highest among all cancers affecting women. Early diagnosis is a predominant factor for survival. Hence techniques to assist the current modalities are essential. Machine learning techniques have been used so as to produce better prediction and classification models which will aid in better and earlier disease diagnosis and classification. Random Forest is a supervised machine learning classifier that helps in better classification. Random Forests are applied to the Wisconsin breast cancer dataset and the performance of the classifier is evaluated for breast cancer classification. Here in this study an improvised random forest model which uses a cost sensitive learning approach for classification is proposed and it is found to have a better performance than the traditional random forest approach. The model gave an accuracy of 97.51%.
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Wang, Zijie, Yufang Bi, Gang Lu, Xu Zhang, Xiangyang Xu, Yilin Ning, Xuhua Du e Anke Wang. "Monitoring Forest Diversity under Moso Bamboo Invasion: A Random Forest Approach". Forests 15, n.º 2 (7 de fevereiro de 2024): 318. http://dx.doi.org/10.3390/f15020318.
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Moso bamboo (Phyllostachys edulis) is a crucial species among the 500 varieties of bamboo found in China and plays an important role in providing ecosystem services. However, remote sensing studies on the invasion of Moso bamboo, especially its impact on forest biodiversity, are limited. Therefore, we explored the feasibility of using Sentinel-2 multispectral data and digital elevation data from the Shuttle Radar Topography Mission and random forest (RF) algorithms to monitor changes in forest diversity due to the spread of Moso bamboo. From October to November 2019, researchers conducted field surveys on 100 subtropical forest plots in Zhejiang Province, China. Four biodiversity indices (Margalef, Shannon, Simpson, and Pielou) were calculated from the survey data. Subsequently, after completing 100 epochs of training and testing, we developed the RF prediction model and assessed its performance using three key metrics: coefficient of determination, root mean squared error, and mean absolute error. Our results showed that the RF model has a strong predictive ability for all indices except for the Pilou index, which has an average predictive ability. These results demonstrate the feasibility of using remote sensing to monitor forest diversity changes caused by the spreading of Moso bamboo.
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Zhou, Shu-Ping, Su-Ding Fei, Hui-Hui Han, Jing-Jing Li, Shuang Yang e Chun-Yang Zhao. "A Prediction Model for Cognitive Impairment Risk in Colorectal Cancer after Chemotherapy Treatment". BioMed Research International 2021 (20 de fevereiro de 2021): 1–13. http://dx.doi.org/10.1155/2021/6666453.
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Background. A prediction model can be developed to predict the risk of cancer-related cognitive impairment in colorectal cancer patients after chemotherapy. Methods. A regression analysis was performed on 386 colorectal cancer patients who had undergone chemotherapy. Three prediction models (random forest, logistic regression, and support vector machine models) were constructed using collected clinical and pathological data of the patients. Calibration and ROC curves and C -indexes were used to evaluate the selected models. A decision curve analysis (DCA) was used to determine the clinical utility of the line graph. Results. Three prediction models including a random forest, a logistic regression, and a support vector machine were constructed. The logistic regression model had the strongest predictive power with an area under the curve (AUC) of 0.799. Age, BMI, colostomy, complications, CRA, depression, diabetes, QLQ-C30 score, exercise, hypercholesterolemia, diet, marital status, education level, and pathological stage were included in the nomogram. The C -index (0.826) and calibration curve showed that the nomogram had good predictive ability and the DCA curves indicated that the model had strong clinical utility. Conclusions. A prediction model with good predictive ability and practical clinical value can be developed for predicting the risk of cognitive impairment in colorectal cancer after chemotherapy.
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ROHAJAWATI, Siti, Hutanti SETYODEWI, Ferryansyah Muji Agustian TRESNANTO, Debora MARIANTHI e Maruli Tua Baja SIHOTANG. "KNOWLEDGE MANAGEMENT APPROACH IN COMPARATIVE STUDY OF AIR POLLUTION PREDICTION MODEL". Applied Computer Science 20, n.º 1 (30 de março de 2024): 173–88. http://dx.doi.org/10.35784/acs-2024-11.
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This study utilizes knowledge management (KM) to highlight a documentation-centric approach that is enhanced through artificial intelligence. Knowledge management can improve the decision-making process for predicting models that involved datasets, such as air pollution. Currently, air pollution has become a serious global issue, impacting almost every major city worldwide. As the capital and a central hub for various activities, Jakarta experiences heightened levels of activity, resulting in increased vehicular traffic and elevated air pollution levels. The comparative study aims to measure the accuracy levels of the naïve bayes, decision trees, and random forest prediction models. Additionally, the study uses evaluation measurements to assess how well the machine learning performs, utilizing a confusion matrix. The dataset’s duration is three years, from 2019 until 2021, obtained through Jakarta Open Data. The study found that the random forest achieved the best results with an accuracy rate of 94%, followed by the decision tree at 93%, and the naïve bayes had the lowest at 81%. Hence, the random forest emerges as a reliable predictive model for prediction of air pollution.
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Yu, Chenghao. "Walmart Sales Forecasting using Different Models". Highlights in Science, Engineering and Technology 92 (10 de abril de 2024): 302–7. http://dx.doi.org/10.54097/kqf76062.
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This research study compares three regression models, namely Random Forest, Linear Regression, and Lasso Regression, to determine which model has better performance on predicting Walmart sales. Through the analysis of historical sales data, including factors such as time, unemployment rate, CPI, and temperature, the dataset have training and testing sets. Random Forest is implemented and compared with Linear Regression, a traditional statistical method, as well as Lasso Regression, which includes a regularization term for feature selection and prediction accuracy improvement. Performance evaluation is conducted using mean squared error,and R-squared score. The results consistently show that Random Forest outperforms both Linear Regression and Lasso Regression in predicting Walmart sales, demonstrating its accuracy and robustness. This research offers insights into predictive modeling in retail sales forecasting and highlights the potential for using Random Forest as a reliable tool for inventory control, demand forecasting, and strategic planning at Walmart and similar retailers. Overall, this study contributes to the understanding of sales prediction in the retail industry, suggesting avenues for future research in exploring advanced machine learning algorithms and data preprocessing techniques to further improve accuracy.
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Yan, Miaomiao, e Yindong Shen. "Traffic Accident Severity Prediction Based on Random Forest". Sustainability 14, n.º 3 (2 de fevereiro de 2022): 1729. http://dx.doi.org/10.3390/su14031729.
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The prediction of traffic accident severity is essential for traffic safety management and control. To achieve high prediction accuracy and model interpretability, we propose a hybrid model that integrates random forest (RF) and Bayesian optimization (BO). In the proposed model, BO-RF, RF is adopted as a basic predictive model and BO is used to tune the parameters of RF. Experimental results show that BO-RF achieves higher accuracy than conventional algorithms. Moreover, BO-RF provides interpretable results by relative importance and a partial dependence plot. We can identify important influential factors for traffic accident severity by relative importance. Further, we can investigate how the influential factors affect traffic accident severity by the partial dependence plot. These results provide insights to mitigate the severity of traffic accident consequences and contribute to the sustainable development of transportation.
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Liu, Qian, Wanyin Qi, Yanping Wu, Yingjun Zhou e Zhiwei Huang. "Construction of Pulmonary Nodule CT Radiomics Random Forest Model Based on Artificial Intelligence Software for STAS Evaluation of Stage IA Lung Adenocarcinoma". Computational and Mathematical Methods in Medicine 2022 (28 de agosto de 2022): 1–6. http://dx.doi.org/10.1155/2022/2173412.
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Objective. Spread through air space (STAS) is an invasive characterization of lung adenocarcinoma and is regarded as a risk factor for poor prognosis. The aim of this study is to develop a random forest model for preoperative prediction of spread through air spaces (STAS) in stage IA lung adenocarcinoma. Methods. 92 patients with stage IA lung adenocarcinoma, who underwent computed tomography (CT) scan and surgical resection, were retrospectively reviewed. Each pulmonary nodule was automatically segmented by artificial intelligence (AI) software, and its CT-based radiomics were extracted. All patients were pathologically classified into STAS-negative and STAS-positive cohorts; then, clinical pathological and CT-based radiomics were compared between the two cohorts. Finally, a prediction model for evaluating STAS status in stage IA lung adenocarcinoma was established by a random forest model. Results. Among 92 patients with stage IA lung adenocarcinoma, STAS positive was identified in 19 patients. The random forest classification model identified predictive features, including CT maximum value, consolidation to tumor ratio (CTR), 3D diameter, CT mean value, entropy, and CT minimum value. The misclassification rate of the random forest model is only 7.69%. Conclusion. The risk factors of STAS in stage IA lung adenocarcinoma can be effectively identified based on the random forest model, and the hierarchical management of characteristic risk can be effectively realized. A random forest model for predicting STAS in IA lung adenocarcinoma is simple and practical.
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Wang, Hao, He Zhang, Jia Zhao, Xinyi Liu, Xinyue Feng e Yinuo Sun. "Product order-demand prediction model based on random forest". Highlights in Business, Economics and Management 18 (15 de outubro de 2023): 383–90. http://dx.doi.org/10.54097/hbem.v18i.12735.
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This study aims to develop a decision support model based on product order data analysis and demand forecasting. By analyzing the shipment data of a large manufacturing enterprise from September 2015 to December 2018, we establish an accurate prediction model for the demand in the next three months of a large manufacturing enterprise. Quarterly and monthly variables capture trends and seasonal variation by adjusting hyperparameters and cross-validation using a random forest algorithm. The results show that the mean absolute error (MAE) on the test set is 8.965, the root mean square error (RMSE) is 11.369, the relative mean absolute error (MAPE) is 8.256%, and the coefficient of determination (R²) is 0.826. These indicators confirm that the model can accurately predict the target variable, with little difference from the true value, and show good predictive power and fit. The monthly model has high accuracy and stability and can effectively support production and supply chain planning to meet future needs. This study confirms the potential of product order data analysis and demand prediction models to improve the efficiency and competitiveness of enterprises and provides a valuable reference for the research and practice in related fields.
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Yadav, Pradeep, Chandra Prakash Bhargava, Deepak Gupta, Jyoti Kumari, Archana Acharya e Madhukar Dubey. "Breast Cancer Disease Prediction Using Random Forest Regression and Gradient Boosting Regression". International Journal of Experimental Research and Review 38 (30 de abril de 2024): 132–46. http://dx.doi.org/10.52756/ijerr.2024.v38.012.
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The current research endeavors to evaluate the efficacy of regression-based machine learning algorithms through an assessment of their performance using diverse metrics. The focus of our study involves the implementation of the breast cancer Wisconsin (Diagnostic) dataset, employing both the random forest and gradient-boosting regression algorithms. In our comprehensive performance analysis, we utilized key metrics such as Mean Squared Error (MSE), R-squared, Mean Absolute Error (MAE), and Coefficient of Determination (COD), supplemented by additional metrics. The evaluation aimed to gauge the algorithms' accuracy and predictive capabilities. Notably, for continuous target variables, the gradient-boosting regression model emerged as particularly noteworthy in terms of performance when compared to other models. The gradient-boosting regression model exhibited remarkable results, highlighting its superiority in handling the breast cancer dataset. The model achieved an impressively low MSE value of 0.05, indicating minimal prediction errors. Furthermore, the R-squared value of 0.89 highlighted the model's ability to explain the variance in the data, affirming its robust predictive power. The Mean Absolute Error (MAE) of 0.14 reinforced the model's accuracy in predicting continuous outcomes. Beyond these core metrics, the study incorporated additional measures to provide a comprehensive understanding of the algorithms' performance. The findings underscore the potential of gradient-boosting regression in enhancing predictive accuracy for datasets with continuous target variables, particularly evident in the context of breast cancer diagnosis. This research contributes valuable insights to the ongoing exploration of machine learning algorithms, providing a basis for informed decision-making in medical and predictive analytics domains.
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Nie, Shunqi, Honghua Chen, Xinxin Sun e Yunce An. "Spatial Distribution Prediction of Soil Heavy Metals Based on Random Forest Model". Sustainability 16, n.º 11 (22 de maio de 2024): 4358. http://dx.doi.org/10.3390/su16114358.
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Mastering the spatial distribution of soil heavy metal content and evaluating the pollution status of soil heavy metals is of great significance for ensuring agricultural production and protecting human health. This study used a machine learning model to study the spatial distribution of soil heavy metal content in a coastal city in eastern China. Having obtained six soil heavy metal contents, including Cr, Cd, Pb, As, Hg, and Ni, environmental variables such as precipitation, soil moisture, and population density were selected. Random forest (RF) was used to model the spatial distribution of soil heavy metal content. The research findings indicate that the RF model demonstrates a robust predictive capability in discerning the spatial distribution of soil heavy metals, and environmental factor variables can explain 60%, 52.3%, 53.5%, 63.1%, 61.2%, and 51.2% of the heavy metal content of Cr, Cd, Pb, As, Hg, and Ni in soil, respectively. Among the chosen environmental variables, precipitation and population density exert notable influences on the predictive outcomes of the model. Specifically, precipitation exhibits the most substantial impact on Cr and Ni, whereas population density emerges as the primary determinant for Cd, Pb, As, and Hg. The RF prediction results show that Cr and Ni in the study area are less affected by human activities, while Cd, Pb, As, and Hg are more affected by human industrial and agricultural production. Research has shown that using RF models for predicting soil heavy metal distributions has certain significance.
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Hujare, Pravin, Praveen Rathod, Dinesh Kamble, Amit Jomde e Shalini Wankhede. "Predictive analytics of disc brake deformation using machine learning". Journal of Information and Optimization Sciences 45, n.º 4 (2024): 1153–63. http://dx.doi.org/10.47974/jios-1699.
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This paper explores the application of machine learning techniques to predict disc brake deformation, a critical aspect in ensuring the safety and reliability of braking systems. The study employs a dataset comprising 50 data points, with input parameters such as pressure and an output parameter of deformation. The focus is on developing predictive models that can accurately estimate disc brake deformation under various operating conditions. To achieve this, four machine learning approaches are investigated and compared: Decision Tree, Random Forest, Support Vector Machine (SVM), and K-Nearest Neighbors (KNN). Each model is trained and evaluated based on its ability to predict deformation, with performance assessed using R-squared metrics. Results indicate notable variations in the predictive capabilities of the models. The Random Forest model emerges as a top performer, demonstrating robustness in capturing complex relationships within the dataset. The Decision Tree model exhibits competitive performance, showcasing its suitability for interpretable predictions. Meanwhile, the SVM model, while effective, exhibits sensitivity to the choice of kernel function. The KNN model, with its simplicity and flexibility, also offers promising results. This research provides valuable insights into the effectiveness of different machine learning approaches in predicting disc brake deformation. It is found that the Random Forest model achieved an accuracy of 99%. These results suggest that Random Forest model are more effective at predicting disc brake deformation than SVMs. The findings contribute to the advancement of intelligent braking systems, enhancing safety and reliability in automotive applications.
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Divya Chilukuri, Akhila Tejaswini. K, Prathyusha. K e Anjali. N. "A review on predictive model for Autisim spectrum disorder". World Journal of Advanced Engineering Technology and Sciences 12, n.º 1 (30 de maio de 2024): 218–21. http://dx.doi.org/10.30574/wjaets.2024.12.1.0204.
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In present day Autism Spectrum Disorder (ASD) is becoming more often, but testing for it is expensive and slow. However, with the help of machine learning (ML) algorithms, we can predict autism in the early stages. So far different Machine Learning algorithms are used to predict Autism in early stages but those are not for both adults and children. So, there is a new prediction model using machine learning by creating a mobile app for predicting autism in people of any age. This model combines two algorithms, Random Forest-CART and Random Forest-ID3, to improve accuracy.
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Bozorgmehr, Arezoo, Anika Thielmann e Birgitta Weltermann. "Chronic stress in practice assistants: An analytic approach comparing four machine learning classifiers with a standard logistic regression model". PLOS ONE 16, n.º 5 (4 de maio de 2021): e0250842. http://dx.doi.org/10.1371/journal.pone.0250842.
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Background Occupational stress is associated with adverse outcomes for medical professionals and patients. In our cross-sectional study with 136 general practices, 26.4% of 550 practice assistants showed high chronic stress. As machine learning strategies offer the opportunity to improve understanding of chronic stress by exploiting complex interactions between variables, we used data from our previous study to derive the best analytic model for chronic stress: four common machine learning (ML) approaches are compared to a classical statistical procedure. Methods We applied four machine learning classifiers (random forest, support vector machine, K-nearest neighbors’, and artificial neural network) and logistic regression as standard approach to analyze factors contributing to chronic stress in practice assistants. Chronic stress had been measured by the standardized, self-administered TICS-SSCS questionnaire. The performance of these models was compared in terms of predictive accuracy based on the ‘operating area under the curve’ (AUC), sensitivity, and positive predictive value. Findings Compared to the standard logistic regression model (AUC 0.636, 95% CI 0.490–0.674), all machine learning models improved prediction: random forest +20.8% (AUC 0.844, 95% CI 0.684–0.843), artificial neural network +12.4% (AUC 0.760, 95% CI 0.605–0.777), support vector machine +15.1% (AUC 0.787, 95% CI 0.634–0.802), and K-nearest neighbours +7.1% (AUC 0.707, 95% CI 0.556–0.735). As best prediction model, random forest showed a sensitivity of 99% and a positive predictive value of 79%. Using the variable frequencies at the decision nodes of the random forest model, the following five work characteristics influence chronic stress: too much work, high demand to concentrate, time pressure, complicated tasks, and insufficient support by practice leaders. Conclusions Regarding chronic stress prediction, machine learning classifiers, especially random forest, provided more accurate prediction compared to classical logistic regression. Interventions to reduce chronic stress in practice personnel should primarily address the identified workplace characteristics.
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Lee, Seung-hyeong, e Eun-Ju Baek. "Development of a predictive model for university students’ core competency index using machine learning: Focusing on D University". Korean Association For Learner-Centered Curriculum And Instruction 22, n.º 11 (15 de junho de 2022): 831–49. http://dx.doi.org/10.22251/jlcci.2022.22.11.831.
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Objectives The purpose of this study was to present implications for the development and operation of the core competency-based curriculum by predicting the core competency of college students.
Methods The panel data of the D-CODA result panel data for the past 3 years (2019-2021) of D-University students located in the Busan area were analyzed. Machine learning prediction models such as multiple linear regression analysis (LR), random forest (RF), and support vector machine (SVM), were used to predict core competencies.
Results The following research results were derived from the study. First, the optimal prediction model for each core competency is as follows. Professional competency was shown in the RF (random forest) model, personality competency in SVM (support vector machine), creative competency in the RF (random forest) model, challenge competency and glocal (global and local) competency in the SVM (support vector machine) model, and communication competency in the LR (multi-linear regression analysis) model. Second, in the analysis of competencies, it was found that professional competency contributes to the prediction of professional competency, and both personality competency and communication competency to that of personality competency. Third, in the model analysis to predict the overall core competency index, the optimal predictive model was found to be the RF (random forest) model which showed the least error. Fourth, in the prediction of key competency indicators in 2022, it is predicted that expertise, personality, creativity, and challenging competency will improve.
Conclusions This study revealed that the analysis using accumulated core competency data and machine learning is useful in predicting and discriminating the core competency of college students. This study is meaningful in that it suggests the importance of periodic core competency index management at the university level and provides the basis for designing a core competency-based curriculum.
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Groll, Andreas, Cristophe Ley, Gunther Schauberger e Hans Van Eetvelde. "A hybrid random forest to predict soccer matches in international tournaments". Journal of Quantitative Analysis in Sports 15, n.º 4 (25 de outubro de 2019): 271–87. http://dx.doi.org/10.1515/jqas-2018-0060.
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Abstract In this work, we propose a new hybrid modeling approach for the scores of international soccer matches which combines random forests with Poisson ranking methods. While the random forest is based on the competing teams’ covariate information, the latter method estimates ability parameters on historical match data that adequately reflect the current strength of the teams. We compare the new hybrid random forest model to its separate building blocks as well as to conventional Poisson regression models with regard to their predictive performance on all matches from the four FIFA World Cups 2002–2014. It turns out that by combining the random forest with the team ability parameters from the ranking methods as an additional covariate the predictive power can be improved substantially. Finally, the hybrid random forest is used (in advance of the tournament) to predict the FIFA World Cup 2018. To complete our analysis on the previous World Cup data, the corresponding 64 matches serve as an independent validation data set and we are able to confirm the compelling predictive potential of the hybrid random forest which clearly outperforms all other methods including the betting odds.
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Mao, Mohan. "A Comparative Study of Random Forest Regression for Predicting House Prices Using". Highlights in Science, Engineering and Technology 85 (13 de março de 2024): 969–74. http://dx.doi.org/10.54097/bdfe8032.
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Based on the rapid development of the real estate market, real estate prices in various regions of the world fluctuate greatly and are unstable, and we need to make some predictions for real estate prices. However, in reality, we pay too much attention to the relationship between past property prices and current property prices and often ignore the prediction of future house prices. Research on predictive models is lacking. Therefore, studying real estate forecasting models is one of the best solutions to solve the problems faced by the real estate market based on the thinking of the current situation. In response to this problem, I propose to use a random forest model, gradient boosting, and optional to build a reasonable predictive model. The final results prove that this predictive model can be used to some extent to predict changing real estate prices in the future market. It is hoped that the method in this paper can provide a reference for subsequent research on predictive models.
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Zhou, Jing, Yuzhen Li e Xuan Guo. "Predicting psoriasis using routine laboratory tests with random forest". PLOS ONE 16, n.º 10 (19 de outubro de 2021): e0258768. http://dx.doi.org/10.1371/journal.pone.0258768.
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Psoriasis is a chronic inflammatory skin disease that affects approximately 125 million people worldwide. It has significant impacts on both physical and emotional health-related quality of life comparable to other major illnesses. Accurately prediction of psoriasis using biomarkers from routine laboratory tests has important practical values. Our goal is to derive a powerful predictive model for psoriasis disease based on only routine hospital tests. We collected a data set including 466 psoriasis patients and 520 healthy controls with 81 variables from only laboratory routine tests, such as age, total cholesterol, HDL cholesterol, blood pressure, albumin, and platelet distribution width. In this study, Boruta feature selection method was applied to select the most relevant features, with which a Random Forest model was constructed. The model was tested with 30 repetitions of 10-fold cross-validation. Our classification model yielded an average accuracy of 86.9%. 26 notable features were selected by Boruta, among which 15 features are confirmed from previous studies, and the rest are worth further investigations. The experimental results demonstrate that the machine learning approach has good potential in predictive modeling for the psoriasis disease given the information only from routine hospital tests.
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Shao, Yakui, Zhongke Feng, Meng Cao, Wenbiao Wang, Linhao Sun, Xuanhan Yang, Tiantian Ma et al. "An Ensemble Model for Forest Fire Occurrence Mapping in China". Forests 14, n.º 4 (29 de março de 2023): 704. http://dx.doi.org/10.3390/f14040704.
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Assessing and predicting forest fires has long been an arduous task. Nowadays, the rapid advancement of artificial intelligence and machine learning technologies have provided a novel solution to forest fire occurrence assessment and prediction. In this research, we developed a novel hybrid machine-learning-technique algorithm to improve forest fire prediction based on random forest (RF), gradient-boosting decision tree (GBDT), support vector machine (SVM), and other machine learning models. The dataset we employed was satellite fire point data from 2010 to 2018 from the Chinese Department of Fire Prevention. The efficacy and performance of our methods were examined by validating the model fit and predictive capability. The results showed that the ensemble model LR (logistic regression)-RF-SVM-GBDT outperformed the single RFSVMGBDT model and the LR-RF-GBDT integrated framework, displaying higher accuracy and greater robustness. We believe that our newly developed hybrid machine-learning algorithm has the potential to improve the accuracy of predicting forest fire occurrences, thus enabling more efficient firefighting efforts and saving time and resources.
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Chao Gao. "Balancing Interpretability and Performance: Optimizing Random Forest Algorithm Based on Point-to-Point Federated Learning". Journal of Electrical Systems 20, n.º 7s (4 de maio de 2024): 2389–400. http://dx.doi.org/10.52783/jes.3990.
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Federated learning is extensively applied in collaborative data scenarios involving multiple data owners. While the majority of state-of-the-art federated learning algorithms are currently black-box models, making it challenging for users to comprehend how decisions are made. Random forest models are extensively utilized in medical contexts owing to their exceptional interpretability. However, when faced with multicenter data, the heterogeneity of data from each center often leads to its predictive performance falling short of expectations. To mitigate this challenge, the present study introduces DFLRF (Decentralized Federated Learning Random Forest), a federated learning algorithm based on random forests. Expanding on conventional random forests, DFLRF employs federated learning to disseminate decision tree models. It assesses and consolidates tree models from all client sites, thereby comprehensively addressing data disparities across various centers. The algorithm selects the optimal decision tree model based on the magnitude of model loss to guarantee the predictive performance of the final federated random forest model. The algorithm undergoes testing on a public dataset. Experimental results demonstrate that, compared to baseline algorithms, DFLRF enhances the AUC by 1.5% and the recall rate by 6%, while also ensuring superior interpretability.
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Truong, Tran Xuan, Viet-Ha Nhu, Doan Thi Nam Phuong, Le Thanh Nghi, Nguyen Nhu Hung, Pham Viet Hoa e Dieu Tien Bui. "A New Approach Based on TensorFlow Deep Neural Networks with ADAM Optimizer and GIS for Spatial Prediction of Forest Fire Danger in Tropical Areas". Remote Sensing 15, n.º 14 (8 de julho de 2023): 3458. http://dx.doi.org/10.3390/rs15143458.
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Frequent forest fires are causing severe harm to the natural environment, such as decreasing air quality and threatening different species; therefore, developing accurate prediction models for forest fire danger is vital to mitigate these impacts. This research proposes and evaluates a new modeling approach based on TensorFlow deep neural networks (TFDeepNN) and geographic information systems (GIS) for forest fire danger modeling. Herein, TFDeepNN was used to create a forest fire danger model, whereas the adaptive moment estimation (ADAM) optimization algorithm was used to optimize the model, and GIS with Python programming was used to process, classify, and code the input and output. The modeling focused on the tropical forests of the Phu Yen Province (Vietnam), which incorporates 306 historical forest fire locations from 2019 to 2023 and ten forest-fire-driving factors. Random forests (RF), support vector machines (SVM), and logistic regression (LR) were used as a baseline for the model comparison. Different statistical metrics, such as F-score, accuracy, and area under the ROC curve (AUC), were employed to evaluate the models’ predictive performance. According to the results, the TFDeepNN model (with F-score of 0.806, accuracy of 79.3%, and AUC of 0.873) exhibits high predictive performance and surpasses the performance of the three baseline models: RF, SVM, and LR; therefore, TFDeepNN represents a novel tool for spatially predicting forest fire danger. The forest fire danger map from this study can be helpful for policymakers and authorities in Phu Yen Province, aiding sustainable land-use planning and management.
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Lee, Soo-Kyoung, Juh Hyun Shin, Jinhyun Ahn, Ji Yeon Lee e Dong Eun Jang. "Identifying the Risk Factors Associated with Nursing Home Residents’ Pressure Ulcers Using Machine Learning Methods". International Journal of Environmental Research and Public Health 18, n.º 6 (13 de março de 2021): 2954. http://dx.doi.org/10.3390/ijerph18062954.
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Background: Machine learning (ML) can keep improving predictions and generating automated knowledge via data-driven predictors or decisions. Objective: The purpose of this study was to compare different ML methods including random forest, logistics regression, linear support vector machine (SVM), polynomial SVM, radial SVM, and sigmoid SVM in terms of their accuracy, sensitivity, specificity, negative predictor values, and positive predictive values by validating real datasets to predict factors for pressure ulcers (PUs). Methods: We applied representative ML algorithms (random forest, logistic regression, linear SVM, polynomial SVM, radial SVM, and sigmoid SVM) to develop a prediction model (N = 60). Results: The random forest model showed the greatest accuracy (0.814), followed by logistic regression (0.782), polynomial SVM (0.779), radial SVM (0.770), linear SVM (0.767), and sigmoid SVM (0.674). Conclusions: The random forest model showed the greatest accuracy for predicting PUs in nursing homes (NHs). Diverse factors that predict PUs in NHs including NH characteristics and residents’ characteristics were identified according to diverse ML methods. These factors should be considered to decrease PUs in NH residents.
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Malhi, Ramandeep Kaur M., Akash Anand, Prashant K. Srivastava, G. Sandhya Kiran, George P. Petropoulos e Christos Chalkias. "An Integrated Spatiotemporal Pattern Analysis Model to Assess and Predict the Degradation of Protected Forest Areas". ISPRS International Journal of Geo-Information 9, n.º 9 (2 de setembro de 2020): 530. http://dx.doi.org/10.3390/ijgi9090530.
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Forest degradation is considered to be one of the major threats to forests over the globe, which has considerably increased in recent decades. Forests are gradually getting fragmented and facing biodiversity losses because of climate change and anthropogenic activities. Future prediction of forest degradation spatiotemporal dynamics and fragmentation is imperative for generating a framework that can aid in prioritizing forest conservation and sustainable management practices. In this study, a random forest algorithm was developed and applied to a series of Landsat images of 1998, 2008, and 2018, to delineate spatiotemporal forest cover status in the sanctuary, along with the predictive model viz. the Cellular Automata Markov Chain for simulating a 2028 forest cover scenario in Shoolpaneshwar Wildlife Sanctuary (SWS), Gujarat, India. The model’s predicting ability was assessed using a series of accuracy indices. Moreover, spatial pattern analysis—with the use of FRAGSTATS 4.2 software—was applied to the generated and predicted forest cover classes, to determine forest fragmentation in SWS. Change detection analysis showed an overall decrease in dense forest and a subsequent increase in the open and degraded forests. Several fragmentation metrics were quantified at patch, class, and landscape level, which showed trends reflecting a decrease in fragmentation in forest areas of SWS for the period 1998 to 2028. The improvement in SWS can be attributed to the enhanced forest management activities led by the government, for the protection and conservation of the sanctuary. To our knowledge, the present study is one of the few focusing on exploring and demonstrating the added value of the synergistic use of the Cellular Automata Markov Chain Model Coupled with Fragmentation Statistics in forest degradation analysis and prediction.
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Saladi, Sarojini Devi, e Radhika Yarlagadda. "An Enhanced Bankruptcy Prediction Model Using Fuzzy Clustering Model and Random Forest Algorithm". Revue d'Intelligence Artificielle 35, n.º 1 (28 de fevereiro de 2021): 77–83. http://dx.doi.org/10.18280/ria.350109.
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The rapid advancements made in Information Technologies (IT) have evolved in the prediction model for financial data. Research in the prediction of bankruptcy is inclining to owe to the growth of related associations with economic and social phenomena. Financial crises in recent scenarios have influenced the growth of financial institutions. Hence, the need for bankruptcy risk prediction at an earlier stage is of prime importance. Though several prediction algorithms were suggested, the predictive models' accuracy is still a challenging task. In this paper, a bankruptcy prediction model is developed by integrating the Fuzzy clustering model and Multi-objective random forest classifiers. The voluminous number of records of the financial dataset and polish bankruptcy dataset is collected from a public repository. It is pre-processed using a MapReduce technique, one of the Big Data approaches. Benefits given by big data approaches help to achieve better flexibility towards a variable declaration. The collected records are pre-processed and organized under efficient index construction. FCM is employed to cluster the data for analytic purposes. Finally, a multi-objective Random Forest classifier helps to develop a prediction model for bankruptcy. Experimental analysis is carried out with accuracy, precision, sensitivity, and specificity compared with existing, Genetic algorithms. Compared to the existing technique, the proposed technique has obtained 80% accuracy.
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Le, Ngoc-Bich, Thi-Thu-Hien Pham, Sy-Hoang Nguyen, Nhat-Minh Nguyen e Tan-Nhu Nguyen. "AI-powered Predictive Model for Stroke and Diabetes Diagnostic". International Journal of Intelligent Systems and Applications 16, n.º 1 (8 de fevereiro de 2024): 24–40. http://dx.doi.org/10.5815/ijisa.2024.01.03.
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Research efforts in the prediction of stroke and diabetes prioritize early detection in order to enhance patient outcomes. To achieve this, a variety of methodologies are integrated. Existing studies, on the other hand, are marred by imbalanced datasets, lack of diversity in their datasets, potential bias, and inadequate model comparisons; these flaws underscore the necessity for more comprehensive and inclusive research methodologies. This paper provides a thorough assessment of machine learning algorithms in the context of early detection and diagnosis of stroke and diabetes. The research employed widely used algorithms, including Logistic Regression, Decision Tree, Random Forest, K-Nearest Neighbors (KNN), and XGBoost Classifier, to examine medical data and derive significant findings. The XGBoost Classifier demonstrated superior performance, with an outstanding accuracy, precision, recall, and F1-score of 87.5%. The comparative examination of the algorithms indicated that the Decision Tree, Random Forest, and XGBoost classifiers consistently exhibited strong performance across all measures. The models demonstrated impressive discrimination capabilities, with the XGBoost Classifier and Random Forest reaching accuracy rates of roughly 87.5% and 86.5% respectively. The Decision Tree Classifier exhibited notable performance, with an accuracy rate of 83%. The overall accuracy of the models was evident in the F1-score, a metric that incorporates recall and precision, where the XGBoost model exhibited a marginal improvement of 2% over the Random Forest and Decision Tree models, and 4.25 percent over the last two. The aforementioned results underscore the effectiveness of the XGBoost Classifier, which will be employed as a predictive model in this study, alongside the Random Forest and Decision Tree models, for the accurate identification of stroke and diabetes. Furthermore, combining datasets improves model performance by utilizing relative features. This integrated dataset improves the model's efficiency and creates a resilient and comprehensive prediction model, improving healthcare outcomes. The findings of this research make a valuable contribution to the advancement of AI-driven diagnostic systems, hence enhancing the quality of healthcare decision-making.
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Ruyssinck, Joeri, Joachim van der Herten, Rein Houthooft, Femke Ongenae, Ivo Couckuyt, Bram Gadeyne, Kirsten Colpaert, Johan Decruyenaere, Filip De Turck e Tom Dhaene. "Random Survival Forests for Predicting the Bed Occupancy in the Intensive Care Unit". Computational and Mathematical Methods in Medicine 2016 (2016): 1–7. http://dx.doi.org/10.1155/2016/7087053.
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Predicting the bed occupancy of an intensive care unit (ICU) is a daunting task. The uncertainty associated with the prognosis of critically ill patients and the random arrival of new patients can lead to capacity problems and the need for reactive measures. In this paper, we work towards a predictive model based on Random Survival Forests which can assist physicians in estimating the bed occupancy. As input data, we make use of the Sequential Organ Failure Assessment (SOFA) score collected and calculated from 4098 patients at two ICU units of Ghent University Hospital over a time period of four years. We compare the performance of our system with a baseline performance and a standard Random Forest regression approach. Our results indicate that Random Survival Forests can effectively be used to assist in the occupancy prediction problem. Furthermore, we show that a group based approach, such as Random Survival Forests, performs better compared to a setting in which the length of stay of a patient is individually assessed.
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Alfraihat, Ausilah, Amer F. Samdani e Sriram Balasubramanian. "Predicting curve progression for adolescent idiopathic scoliosis using random forest model". PLOS ONE 17, n.º 8 (11 de agosto de 2022): e0273002. http://dx.doi.org/10.1371/journal.pone.0273002.
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Background Adolescent Idiopathic Scoliosis (AIS) is a three-dimensional (3D) spinal deformity characterized by coronal curvature and rotational deformity. Predicting curve progression is important for the selection and timing of treatment. Although there is a consensus in the literature regarding prognostic factors associated with curve progression, the order of importance, as well as the combination of factors that are most predictive of curve progression is unknown. Objectives (1) create an ordered list of prognostic factors that most contribute to curve progression, and (2) develop and validate a Machine Learning (ML) model to predict the final major Cobb angle in AIS patients. Methods 193 AIS patients were selected for the current study. Preoperative PA, lateral and lateral bending radiographs were retrospectively obtained from the Shriners Hospitals for Children. Demographic and radiographic features, previously reported to be associated with curve progression, were collected. Sequential Backward Floating Selection (SBFS) was used to select a subset of the most predictive features. Based on the performance of several machine learning methods, a Random Forest (RF) regressor model was used to provide the importance rank of prognostic features and to predict the final major Cobb angle. Results The seven most predictive prognostic features in the order of importance were initial major Cobb angle, flexibility, initial lumbar lordosis angle, initial thoracic kyphosis angle, age at last visit, number of levels involved, and Risser "+" stage at the first visit. The RF model predicted the final major Cobb angle with a Mean Absolute Error (MAE) of 4.64 degrees. Conclusion A RF model was developed and validated to identify the most important prognostic features for curve progression and predict the final major Cobb angle. It is possible to predict the final major Cobb angle value within 5 degrees error from 2D radiographic features. Such methods could be directly applied to guide intervention timing and optimization for AIS treatment.
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Lei, Xiaoli. "Resource Sharing Algorithm of Ideological and Political Course Based on Random Forest". Mathematical Problems in Engineering 2022 (21 de maio de 2022): 1–8. http://dx.doi.org/10.1155/2022/8765166.
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Three aspects of the system’s online resource distribution and application are built around subject, object, and intermediary resources. The invention relates to a method for allocating resources based on the random forest algorithm. The resource allocation process entails the following steps: constructing a mathematical model of the resource allocation process, defining a mathematical model of the resource allocation process for the target object, and designing the cost function. The training data set for random forest is constructed using the classification concept. It is based on the mathematical model of resource allocation and cost function. Generation of random forests and prediction of target objects are based on historical data. Resource allocation steps are based on predictive structure. The invention provides a resource allocation method that satisfies task completion degree constraints and includes a resource allocation algorithm based on random forest with a high probability of finding an optimal solution. It also addresses the issue that intelligent optimization algorithms such as genetic algorithms are prone to fall into local optimum.
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Qi, Yuxuan. "Research on Stock Price Prediction Based on LSTM Model and Random Forest". Advances in Economics, Management and Political Sciences 86, n.º 1 (28 de maio de 2024): 35–42. http://dx.doi.org/10.54254/2754-1169/86/20240938.
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In this study, cutting-edge methods of applying deep learning techniques to stock market predictions were explored, specifically focusing on the stock data of Tesla Inc. Long Short-Term Memory networks (LSTMs), an advanced form of Recurrent Neural Networks (RNNs) capable of effectively addressing the issues of vanishing and exploding gradients that traditional RNNs face, were employed. This enhances the model's learning capability and predictive accuracy for time series data. The innovation of this research lies in the integration of the LSTM model with the Random Forest algorithm, forming a hybrid model aimed at leveraging the complementary strengths of both models to improve the accuracy of stock price predictions. Through empirical analysis of Tesla's stock data, it was found that the hybrid model outperformed the individual LSTM model. This result not only proved the effectiveness of LSTMs in handling complex time series prediction problems but also demonstrated the potential of enhancing predictive performance by integrating different types of models. The findings offer a new perspective for financial market analysis and prediction, especially in the use of deep learning technologies for stock price forecasting. They provide valuable references for future research and practice in this field. Further investigations could explore the applicability of this hybrid approach to other financial instruments and markets.
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Bayramli, Ilkin, Victor Castro, Yuval Barak-Corren, Emily M. Madsen, Matthew K. Nock, Jordan W. Smoller e Ben Y. Reis. "Temporally informed random forests for suicide risk prediction". Journal of the American Medical Informatics Association 29, n.º 1 (2 de novembro de 2021): 62–71. http://dx.doi.org/10.1093/jamia/ocab225.
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Abstract Objective Suicide is one of the leading causes of death worldwide, yet clinicians find it difficult to reliably identify individuals at high risk for suicide. Algorithmic approaches for suicide risk detection have been developed in recent years, mostly based on data from electronic health records (EHRs). Significant room for improvement remains in the way these models take advantage of temporal information to improve predictions. Materials and Methods We propose a temporally enhanced variant of the random forest (RF) model—Omni-Temporal Balanced Random Forests (OT-BRFs)—that incorporates temporal information in every tree within the forest. We develop and validate this model using longitudinal EHRs and clinician notes from the Mass General Brigham Health System recorded between 1998 and 2018, and compare its performance to a baseline Naive Bayes Classifier and 2 standard versions of balanced RFs. Results Temporal variables were found to be associated with suicide risk: Elevated suicide risk was observed in individuals with a higher total number of visits as well as those with a low rate of visits over time, while lower suicide risk was observed in individuals with a longer period of EHR coverage. RF models were more accurate than Naive Bayesian classifiers at predicting suicide risk in advance (area under the receiver operating curve = 0.824 vs. 0.754, respectively). The proposed OT-BRF model performed best among all RF approaches, yielding a sensitivity of 0.339 at 95% specificity, compared to 0.290 and 0.286 for the other 2 RF models. Temporal variables were assigned high importance by the models that incorporated them. Discussion We demonstrate that temporal variables have an important role to play in suicide risk detection and that requiring their inclusion in all RF trees leads to increased predictive performance. Integrating temporal information into risk prediction models helps the models interpret patient data in temporal context, improving predictive performance.
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Bayramli, Ilkin, Victor Castro, Yuval Barak-Corren, Emily M. Madsen, Matthew K. Nock, Jordan W. Smoller e Ben Y. Reis. "Temporally informed random forests for suicide risk prediction". Journal of the American Medical Informatics Association 29, n.º 1 (2 de novembro de 2021): 62–71. http://dx.doi.org/10.1093/jamia/ocab225.
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Abstract Objective Suicide is one of the leading causes of death worldwide, yet clinicians find it difficult to reliably identify individuals at high risk for suicide. Algorithmic approaches for suicide risk detection have been developed in recent years, mostly based on data from electronic health records (EHRs). Significant room for improvement remains in the way these models take advantage of temporal information to improve predictions. Materials and Methods We propose a temporally enhanced variant of the random forest (RF) model—Omni-Temporal Balanced Random Forests (OT-BRFs)—that incorporates temporal information in every tree within the forest. We develop and validate this model using longitudinal EHRs and clinician notes from the Mass General Brigham Health System recorded between 1998 and 2018, and compare its performance to a baseline Naive Bayes Classifier and 2 standard versions of balanced RFs. Results Temporal variables were found to be associated with suicide risk: Elevated suicide risk was observed in individuals with a higher total number of visits as well as those with a low rate of visits over time, while lower suicide risk was observed in individuals with a longer period of EHR coverage. RF models were more accurate than Naive Bayesian classifiers at predicting suicide risk in advance (area under the receiver operating curve = 0.824 vs. 0.754, respectively). The proposed OT-BRF model performed best among all RF approaches, yielding a sensitivity of 0.339 at 95% specificity, compared to 0.290 and 0.286 for the other 2 RF models. Temporal variables were assigned high importance by the models that incorporated them. Discussion We demonstrate that temporal variables have an important role to play in suicide risk detection and that requiring their inclusion in all RF trees leads to increased predictive performance. Integrating temporal information into risk prediction models helps the models interpret patient data in temporal context, improving predictive performance.
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Wang, Shihao, e Xiangxiang Wu. "The Mechanical Performance Prediction of Steel Materials based on Random Forest". Frontiers in Computing and Intelligent Systems 6, n.º 1 (27 de novembro de 2023): 1–3. http://dx.doi.org/10.54097/fcis.v6i1.01.
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The mechanical performance of steel materials is crucial for the design, selection, and application of materials. In order to better predict the mechanical performance through chemical composition and process parameters, this paper establishes a predictive model for the mechanical properties of steel materials based on the random forest algorithm. The model predicts yield strength, tensile strength, and elongation based on chemical composition and process parameters. The results indicate that the random forest algorithm model demonstrates excellent performance in predicting the mechanical properties of steel materials.
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Nikolopoulos, Efthymios I., Elisa Destro, Md Abul Ehsan Bhuiyan, Marco Borga e Emmanouil N. Anagnostou. "Evaluation of predictive models for post-fire debris flow occurrence in the western United States". Natural Hazards and Earth System Sciences 18, n.º 9 (4 de setembro de 2018): 2331–43. http://dx.doi.org/10.5194/nhess-18-2331-2018.
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Abstract. Rainfall-induced debris flows in recently burned mountainous areas cause significant economic losses and human casualties. Currently, prediction of post-fire debris flows is widely based on the use of power-law thresholds and logistic regression models. While these procedures have served with certain success in existing operational warning systems, in this study we investigate the potential to improve the efficiency of current predictive models with machine-learning approaches. Specifically, the performance of a predictive model based on the random forest algorithm is compared with current techniques for the prediction of post-fire debris flow occurrence in the western United States. The analysis is based on a database of post-fire debris flows recently published by the United States Geological Survey. Results show that predictive models based on random forest exhibit systematic and considerably improved performance with respect to the other models examined. In addition, the random-forest-based models demonstrated improvement in performance with increasing training sample size, indicating a clear advantage regarding their ability to successfully assimilate new information. Complexity, in terms of variables required for developing the predictive models, is deemed important but the choice of model used is shown to have a greater impact on the overall performance.
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Gold, Ochim, e Agaji Iorshase. "Heart failure prediction framework using random forest and J48 with Adaboost algorithms". Science World Journal 18, n.º 2 (20 de outubro de 2023): 165–75. http://dx.doi.org/10.4314/swj.v18i2.1.
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Heart failure is a very serious condition in health sector globally. It has proven difficult and expensive to manage over the years even with some pre-existing prediction models that signal its occurrence. The predictive accuracies of the existing models are below impressive hence the need for better heart failure predictive models. This work developed two heart failure predictive models to contribute to the decrease in the mortality rate due to heart failure as well as assist patients and physicians in managing the condition. The models were Random Forest(RF) and J48 model with AdaBoost. The dataset for the work was collected from the Cleveland Hospital database. It has 13 attributes and 303 instances. The dataset was preprocessed before use and was converted to the CSV format usable in the Waikato Environment for Knowledge Analysis (WEKA) software. The Agile Unified Process (AUP) methodology was adopted in this work the simulator for the work. The Simulator (web-based) was implemented using Python programming language and the Streamlit for python. The result of the models showed a 92.3% accuracy in prediction for the AdaBoosted J48 model and 89.2% for the Random Forest model. The results indicated that J48 with AdaBoost outperformed RF.
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Guo, Shengnan, e Jianqiu Xu. "CPRQ: Cost Prediction for Range Queries in Moving Object Databases". ISPRS International Journal of Geo-Information 10, n.º 7 (8 de julho de 2021): 468. http://dx.doi.org/10.3390/ijgi10070468.
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Predicting query cost plays an important role in moving object databases. Accurate predictions help database administrators effectively schedule workloads and achieve optimal resource allocation strategies. There are some works focusing on query cost prediction, but most of them employ analytical methods to obtain an index-based cost prediction model. The accuracy can be seriously challenged as the workload of the database management system becomes more and more complex. Differing from the previous work, this paper proposes a method called CPRQ (Cost Prediction of Range Query) which is based on machine-learning techniques. The proposed method contains four learning models: the polynomial regression model, the decision tree regression model, the random forest regression model, and the KNN (k-Nearest Neighbor) regression model. Using R-squared and MSE (Mean Squared Error) as measurements, we perform an extensive experimental evaluation. The results demonstrate that CPRQ achieves high accuracy and the random forest regression model obtains the best predictive performance (R-squared is 0.9695 and MSE is 0.154).
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Fernández-Carrillo, Ángel, Antonio Franco-Nieto, María Julia Yagüe-Ballester e Marta Gómez-Giménez. "Predictive Model for Bark Beetle Outbreaks in European Forests". Forests 15, n.º 7 (27 de junho de 2024): 1114. http://dx.doi.org/10.3390/f15071114.
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Bark beetle outbreaks and forest mortality have rocketed in European forests because of warmer winters, intense droughts, and poor management. The methods developed to predict a bark beetle outbreak have three main limitations: (i) a small-spatial-scale implementation; (ii) specific field-based input datasets that are usually hard to obtain at large scales; and (iii) predictive models constrained by coarse climatic factors. Therefore, a methodological approach accounting for a comprehensive set of environmental traits that can predict a bark beetle outbreak accurately is needed. In particular, we aimed to (i) analyze the influence of environmental traits that cause bark beetle outbreaks; (ii) compare different machine learning architectures for predicting bark beetle attacks; and (iii) map the attack probability before the start of the bark beetle life cycle. Random Forest regression achieved the best-performing results. The predicted bark beetle damage reached a high robustness in the test area (F1 = 96.9, OA = 94.4) and showed low errors (CE = 2.0, OE = 4.2). Future improvements should focus on including additional variables, e.g., forest age and validation sites. Remote sensing-based methods contributed to detecting bark beetle outbreaks in large extensive forested areas in a cost-effective and robust manner.
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ERSHOV, EVGENY V., OLGA V. YUDINA, LYUDMILA N. VINOGRADOVA e NIKITA I. SHAKHANOV. "EQUIPMENT CONDITION MODELING BASED ON RANDOM FOREST AND ARIMA MACHINE LEARNING ALGORITHM STACKING". Cherepovets State University Bulletin 4, n.º 97 (2020): 32–40. http://dx.doi.org/10.23859/1994-0637-2020-4-97-3.
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The article discusses algorithms for constructing predicative models of the industrial equipment condition using data analysis and machine learning. The model is based on Random Forest (RF) and ARIMA (AR) algorithms. The authors consider approaches to learning algorithms and optimizing parameters. A block diagram of a time series predictive model applying stacking is presented, as well as an assessment of the simulation results.
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Huan, Juan, Bo Chen, Xian Gen Xu, Hui Li, Ming Bao Li e Hao Zhang. "River Dissolved Oxygen Prediction Based on Random Forest and LSTM". Applied Engineering in Agriculture 37, n.º 5 (2021): 901–10. http://dx.doi.org/10.13031/aea.14496.
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HighlightsRandom Forest (RF) and LSTM were developed for river DO prediction.PH is the most important feature affecting DO prediction.The model base on RF is better than the model not on RF, and the dimensionality of the input data is reduced by RF.RF-LSTM model is outperformed SVR, RF-SVR, BP, RF-BP, LSTM, RNN models in DO prediction.Abstract. In order to improve the prediction accuracy of dissolved oxygen in rivers, a dissolved oxygen prediction model based on Random Forest (RF) and Long Short Term Memory networks (LSTM) is proposed. First, the Random Forest performs feature selection, which reduces the input dimension of the data and eliminates the influence of irrelevant variables on the prediction of dissolved oxygen. Then build the LSTM river dissolved oxygen prediction model to fit the relationship between water quality data and dissolved oxygen, and finally use real water quality data in the river for verification. The experimental results show that the mean square error (MSE), absolute error (MAE), mean absolute percentage error (MAPE), root mean square error (RMSE), and coefficient of determination (R2) of the RF-LSTM model are 0.658, 0.528, 13.502, 0.811, 0.744, respectively, which are better than other models. The RF-LSTM model has good predictive performance and can provide a reference for river water quality management. Keywords: Dissolved oxygen prediction, LSTM, Random forest, Time series, Water quality management.
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Qu, Chaoran, Xiufen Yang, Weisi Peng, Xiujuan Wang e Weixiang Luo. "THE PREDICTIVE EFFECT OF DIFFERENT MACHINE LEARNING ALGORITHMS FOR PRESSURE INJURIES: A NETWORK META-ANALYSES". Innovation in Aging 7, Supplement_1 (1 de dezembro de 2023): 1178. http://dx.doi.org/10.1093/geroni/igad104.3776.
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Abstract Objective This review aims to systematically synthesize existing evidence to determine the effectiveness of applying machine learning algorithms for pressure injury management, to further evaluate and compare pressure injury prediction models constructed by numerous machine learning algorithms, and to derive evidence for the best algorithms for predicting and managing pressure injuries. Methods A systematic electronic search was conducted in the EBSCO, Embase, PubMed, and Web of Science databases. We included all retrospective diagnostic accuracy trials and prospective diagnostic accuracy trials constructing a predictive model by machine learning for pressure injuries up to December 2021. The network meta-analysis was conducted using statistical software R and STATA. The certainty of the evidence was rated using the QUADAS-2 tool. Result Twenty-five clinical diagnostic trials with a total of 237397 participants were identified in this review. The results of our study revealed that pressure injury machine learning models can effectively predict these injuries. Combining the algorithms separately yields the main results decision trees (sensitivity: 0.66, specificity: 0.90, AUC: 0.88), logistic regression (sensitivity: 0.71, specificity: 0.83, AUC: 0.84), neural networks (sensitivity: 0.73, specificity: 0.78, AUC: 0.82), random forests (sensitivity: 0.72, specificity: 0.96, AUC: 0.95), support vector machines (sensitivity: 0.81, specificity: 0.81, AUC: 0.88). According to the analysis of ROC and AUC values, random forest is the best algorithm for the prediction model of pressure injury. Conclusions This review revealed that machine learning algorithms are generally effective in predicting pressure injuries, the random forest algorithm is the best algorithm for pressure injury prediction.