Academic literature on the topic 'Random Forest, Questionnaire, Cardiovascular diseases'
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Journal articles on the topic "Random Forest, Questionnaire, Cardiovascular diseases"
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Butkevičiūtė, Eglė, Liepa Bikulčienė, and Aušra Žvironienė. "Physiological State Evaluation in Working Environment Using Expert System and Random Forest Machine Learning Algorithm." Healthcare 11, no. 2 (January 11, 2023): 220. http://dx.doi.org/10.3390/healthcare11020220.
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Healthy lifestyle is one of the most important factors in the prevention of premature deaths, chronic diseases, productivity loss, obesity, and other economic and social aspects. The workplace plays an important role in promoting the physical activity and wellbeing of employees. Previous studies are mostly focused on individual interviews, various questionnaires that are a conceptual information about individual health state and might change according to question formulation, specialist competence, and other aspects. In this paper the work ability was mostly related to the employee’s physiological state, which consists of three separate systems: cardiovascular, muscular, and neural. Each state consists of several exercises or tests that need to be performed one after another. The proposed data transformation uses fuzzy logic and different membership functions with three or five thresholds, according to the analyzed physiological feature. The transformed datasets are then classified into three stages that correspond to good, moderate, and poor health condition using machine learning techniques. A three-part Random Forest method was applied, where each part corresponds to a separate system. The obtained testing accuracies were 93%, 87%, and 73% for cardiovascular, muscular, and neural human body systems, respectively. The results indicate that the proposed work ability evaluation process may become a good tool for the prevention of possible accidents at work, chronic fatigue, or other health problems.
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Basnet, Til Bahadur, Srijana G. C., Rajesh Basnet, and Bidusha Neupane. "Dietary nutrients of relative importance associated with coronary artery disease: Public health implication from random forest analysis." PLOS ONE 15, no. 12 (December 10, 2020): e0243063. http://dx.doi.org/10.1371/journal.pone.0243063.
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Dietary nutrients have significant effects on the risk of cardiovascular diseases. However, the results were not uniform across different countries. The study aims to determine the relative importance of dietary nutrients associated with coronary artery disease (CAD) among the Nepalese population. A hospital-based matched case-control study was carried out at Shahid Gangalal National Heart Center in Nepal. In the present study, patients with more than seventy percent stenosis in any main coronary artery branch in angiography were defined as cases, while those presenting normal coronary angiography or negative for stressed exercise test were considered controls. Dietary intakes of 612 respondents over the past 12 months were evaluated using a semi-quantitative customized food frequency questionnaire. In conditional regression model, the daily average dietary intake of β-carotene (OR: 0.54; 95%CI: 0.34, 0.87), and vitamin C (OR: 0.96; 95%CI: 0.93, 0.99) were inversely, whereas dietary carbohydrate (OR: 1.16; 95%CI: 1.1, 1.24), total fat/oil (OR: 1.47; 95%CI: 1.27, 1.69), saturated fatty acid (SFA) (OR: 1.2; 95%CI: 1.11, 1.3), cholesterol (OR: 1.01; 95%CI: 1.001, 1.014), and iron intakes (OR: 1.11; 95%CI: 1.03, 1.19) were positively linked with CAD. Moreover, in random forest analysis, the daily average dietary intakes of SFA, vitamin A, total fat/oil, β-carotene, and cholesterol were among the top five nutrients (out of 12 nutrients variables) of relative importance associated with CAD. The nutrients of relative importance imply a reasonable preventive measure in public health nutrients specific intervention to prevent CAD in a resource-poor country like Nepal. The findings are at best suggestive of a possible relationship between these nutrients and the development of CAD, but prospective cohort studies and randomized control trials will need to be performed in the Nepalese population.
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R., Vasanthi,, and Tamilselvi, J. "Heart Disease Prediction Using Random Forest Algorithm." CARDIOMETRY, no. 24 (November 30, 2022): 982–88. http://dx.doi.org/10.18137/cardiometry.2022.24.982988.
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Heart disease is one of the complex diseases and globally many of us suffer from this disease. On time and efficient identification of cardiovascular disease plays a key role in healthcare, particularly within the field of cardiology. An efficient and accurate system to diagnose cardiovascular disease and the system is predicated on machine learning techniques. The system is developed by classification algorithms using Random Forest, Naïve Bayes and Support Vector Machine while standard features selection techniques are used like univerate, feature importance , and correlation matrix for removing irrelevant and redundant features. The features selection are used for feature to extend the classification accuracy and reduce the execution time of the arrangement. The way that aims at finding significant features by applying machine learning techniques leading to improving the accuracy within the prediction of disorder. The heart disease prediction that Random Forest achieved good accuracy as compared to other algorithms.
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Srınıvasa Rao, B. "A New Ensenble Learning based Optimal Prediction Model for Cardiovascular Diseases." E3S Web of Conferences 309 (2021): 01007. http://dx.doi.org/10.1051/e3sconf/202130901007.
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The present paperreports an optimal machine learning model for an effective prediction of cardiovascular diseases that uses the ensemble learning technique. The present research work gives an insight about the coherent way of combining Naive Bayes and Random Forest algorithm using ensemble technique. It also discusses how the present model is different from other traditional approaches. The present experimental results manifest that the present optimal machine learning model is more efficient than the other models.
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Worachartcheewan, Apilak, Watshara Shoombuatong, Phannee Pidetcha, Wuttichai Nopnithipat, Virapong Prachayasittikul, and Chanin Nantasenamat. "Predicting Metabolic Syndrome Using the Random Forest Method." Scientific World Journal 2015 (2015): 1–10. http://dx.doi.org/10.1155/2015/581501.
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Aims. This study proposes a computational method for determining the prevalence of metabolic syndrome (MS) and to predict its occurrence using the National Cholesterol Education Program Adult Treatment Panel III (NCEP ATP III) criteria. The Random Forest (RF) method is also applied to identify significant health parameters.Materials and Methods. We used data from 5,646 adults aged between 18–78 years residing in Bangkok who had received an annual health check-up in 2008. MS was identified using the NCEP ATP III criteria. The RF method was applied to predict the occurrence of MS and to identify important health parameters surrounding this disorder.Results. The overall prevalence of MS was 23.70% (34.32% for males and 17.74% for females). RF accuracy for predicting MS in an adult Thai population was 98.11%. Further, based on RF, triglyceride levels were the most important health parameter associated with MS.Conclusion. RF was shown to predict MS in an adult Thai population with an accuracy >98% and triglyceride levels were identified as the most informative variable associated with MS. Therefore, using RF to predict MS may be potentially beneficial in identifying MS status for preventing the development of diabetes mellitus and cardiovascular diseases.
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Bhatt, Chintan M., Parth Patel, Tarang Ghetia, and Pier Luigi Mazzeo. "Effective Heart Disease Prediction Using Machine Learning Techniques." Algorithms 16, no. 2 (February 6, 2023): 88. http://dx.doi.org/10.3390/a16020088.
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The diagnosis and prognosis of cardiovascular disease are crucial medical tasks to ensure correct classification, which helps cardiologists provide proper treatment to the patient. Machine learning applications in the medical niche have increased as they can recognize patterns from data. Using machine learning to classify cardiovascular disease occurrence can help diagnosticians reduce misdiagnosis. This research develops a model that can correctly predict cardiovascular diseases to reduce the fatality caused by cardiovascular diseases. This paper proposes a method of k-modes clustering with Huang starting that can improve classification accuracy. The underlying model applies DBSCAN to remove outliers. Models such as random forest (RF), decision tree classifier (DT), multilayer perceptron (MP), and XGBoost (XGB) are used. GridSearchCV was used to hypertune the parameters of the applied model to optimize the result. The proposed model is applied to a real-world dataset of 70,000 instances from Kaggle. Models were trained on data that were split in 80:20 and achieved accuracy as follows: decision tree: 86.37% (with cross-validation) and 86.53% (without cross-validation), XGBoost: 86.87% (with cross-validation) and 87.02% (without cross-validation), random forest: 87.05% (with cross-validation) and 86.92% (without cross-validation), multilayer perceptron: 87.28% (with cross-validation) and 86.94% (without cross-validation). The proposed models have AUC (area under the curve) values: decision tree: 0.94, XGBoost: 0.95, random forest: 0.95, multilayer perceptron: 0.95. The conclusion drawn from this underlying research is that multilayer perceptron with cross-validation has outperformed all other algorithms in terms of accuracy. It achieved the highest accuracy of 87.28%.
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Yekkala, Indu, and Sunanda Dixit. "Prediction of Heart Disease Using Random Forest and Rough Set Based Feature Selection." International Journal of Big Data and Analytics in Healthcare 3, no. 1 (January 2018): 1–12. http://dx.doi.org/10.4018/ijbdah.2018010101.
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Data is generated by the medical industry. Often this data is of very complex nature—electronic records, handwritten scripts, etc.—since it is generated from multiple sources. Due to the Complexity and sheer volume of this data necessitates techniques that can extract insight from this data in a quick and efficient way. These insights not only diagnose the diseases but also predict and can prevent disease. One such use of these techniques is cardiovascular diseases. Heart disease or coronary artery disease (CAD) is one of the major causes of death all over the world. Comprehensive research using single data mining techniques have not resulted in an acceptable accuracy. Further research is being carried out on the effectiveness of hybridizing more than one technique for increasing accuracy in the diagnosis of heart disease. In this article, the authors worked on heart stalog dataset collected from the UCI repository, used the Random Forest algorithm and Feature Selection using rough sets to accurately predict the occurrence of heart disease
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Sun, Weicheng, Ping Zhang, Zilin Wang, and Dongxu Li. "Prediction of Cardiovascular Diseases based on Machine Learning." ASP Transactions on Internet of Things 1, no. 1 (May 29, 2021): 30–35. http://dx.doi.org/10.52810/tiot.2021.100035.
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With the rapid development of artificial intelligence, it is very important to find the pattern of the data from the observed data and the functional dependency relationship between the data. By finding the existing functional dependencies, we can classify and predict them. At present, cardiovascular disease has become a major disease harmful to human health. As a disease with high mortality, the prediction problem of cardiovascular disease is becoming more and more urgent. However, some computer methods are mainly used for disease detection rather than prediction. If the computer method can be used to predict cardiovascular disease in advance and treat it as early as possible, then the consequences of the disease can be reduced to a certain extent. Diseases can be predicted by mechanical methods. Support vector machine (SVM) has strict mathematical theory support, and can deal with nonlinear classification after using kernel techniques. Therefore, support vector machine can be used to predict cardiovascular disease. On the other hand, we also use logical regression and random forest to predict cardiovascular disease. This paper mainly uses the method of machine learning to predict whether the population is sick or not. First of all, we preprocess the obtained data to improve the quality of the data, and then use svm and logical regression to predict, so as to provide reference for the prevention and treatment of cardiovascular diseases.
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Osemeobo, Gbadebo Jonathan. "Can Food Crop Medicine Reduce Pressure on Forest Harvest in Nigeria?" Dutse Journal of Pure and Applied Sciences 7, no. 3a (January 3, 2022): 23–31. http://dx.doi.org/10.4314/dujopas.v7i3a.3.
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Awareness created in the social media through smart phones had popularized the practice of using food crop parts such as leaves, fruits, roots and seeds to treat common illness which had hitherto been treated with herbs from the wild. This study investigated whether the use of food crops for medicine can reduce pressures of harvesting medicinal plants from the forest. A combination of three research methods: structured questionnaire survey; informal discussions with stakeholders in food crop medicines and observations on collection and preparation of food crop medicines were used to collect data. Simple random sampling method was used to select 62 respondents for questionnaire survey and discussion groups in Ota Ogun State, Nigeria. Data were presented in tables and percentages. A Chi-square analysis was used to test the research hypothesis. Results derived from data analyses indicated that food crop medicine (FCM) was: (i) widely used; (ii) fully accepted; (iii) gradually reducing pressures in natural forests; and (iv) used to compliment indigenous traditional medicine. A conclusion was reached that FCM had come to stay as a major primary health delivery. Moreover, FCM has found a place in herbal treatments of diseases.
Keywords: Food crop medicine, Traditional medicine, Traditional plants, Natural forests, Herbs.
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Navarrete, Jean Paul, Jose Pinto, Rosa Liliana Figueroa, Maria Elena Lagos, Qing Zeng, and Carla Taramasco. "Supervised Learning Algorithm for Predicting Mortality Risk in Older Adults Using Cardiovascular Health Study Dataset." Applied Sciences 12, no. 22 (November 14, 2022): 11536. http://dx.doi.org/10.3390/app122211536.
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Multiple chronic conditions are an important factor influencing mortality in older adults. At the same time, cardiovascular events in older adult patients are one of the leading causes of mortality worldwide. This study aimed to design a machine learning model capable of predicting mortality risk in older adult patients with cardiovascular pathologies and multiple chronic diseases using the Cardiovascular Health Study database. The methodology for algorithm design included (i) database analysis, (ii) variable selection, (iii) feature matrix creation and data preprocessing, (iv) model training, and (v) performance analysis. The analysis and variable selection were performed through previous knowledge, correlation, and histograms to visualize the data distribution. The machine learning models selected were random forest, support vector machine, and logistic regression. The models were trained using two sets of variables. First, eight years of the data were summarized as the mode of all years per patient for each variable (123 variables). The second set of variables was obtained from the mode every three years (369 variables). The results show that the random forest trained with the second set of variables has the best performance (89% accuracy), which is better than other reported results in the literature.
Dissertations / Theses on the topic "Random Forest, Questionnaire, Cardiovascular diseases"
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BERTU', LORENZA. "Indicatori socio-occupazionali, psicologici e nuovi biomarcatori nella predizione di eventi cardiovascolari maggiori." Doctoral thesis, Università degli Studi di Milano-Bicocca, 2016. http://hdl.handle.net/10281/105042.
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Introduction. The role of psychosocial aspects in the onset of the cardiovascular disease is known but, it is not clear which features are more associated with cardiovascular risk and if they can improve the predictive capability of the classic models based on age, sex, smoking, diabetes, blood pressure and cholesterol. The study of the association among the risk of an event and the people's characteristics is, generally, conducted using two approaches: i) stochastic data modeling, ii) algorithmic modeling. In the field of epidemiology, we have always preferred the first approach because the results are interpretable by the probabilistic point of view, however, it is based on parametric assumptions that often fail to identify factors or interaction between important factors. The second approach, based on statistical learning techniques (Random Forest) is promising in terms of selection of the variables and identification of possible interactions between them.
Objective. To identify items of psychosocial scale most involved in the prediction of major cardiovascular events and to evaluate the association between events and the selected items, by integrating, where possible, the information with biomarkers data.
Methods. The analysis for the selection of items by using the techniques of statistical learning was conducted on 6567 individuals belonging to the cohorts MONICA-Brianza and PAMELA, aged 25-64 years who, during a median follow-up of 15 years, have experienced 527 events. The technique of random forests has been used during the selection of the items psychosocial. Since one of the main problems of random forests is the difficulty of grasping signals in the presence of strongly unbalanced data, we analyzed a dataset in which each event is paired with a non-event of the same sex, age (± 5 years) and under observation at the onset of the event. We identified the number and type of item most associated with the event of interest and we use them in a Cox proportional hazards model aimed at both the risk assessment and the evaluation of their contribution. The additional contribution of the psychosocial item was measured in terms of increase in the index of discrimination (c-index).
Results. The analysis with random forests highlighted as potential predictors of cardiovascular risk 2 item related to Jenkins sleep questionnaire, 4 items related to the Jenkins Activity Survey, and two items related to the Job Content Questionnaire. These items lead to 1.3% increase in AUC when inserted into a model with age, sex, and major risk factors.
Conclusions. The results suggest that the measurement of some psychosocial aspects is important in predicting the risk of cardiovascular event. The mixed strategy used to develop the risk model (algorithmic in the variable selection and the stochastic one for the estimation of the risk) is able to make the most of the features of the two approaches: less constrained to distributional assumptions and linearity of the first, the most suitable to provide an estimate interpretable in terms of the risk the second one.
Book chapters on the topic "Random Forest, Questionnaire, Cardiovascular diseases"
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Jadaun, Ranjana, Shreya Jain, Jyoti Prajapati, Nitin Sharma, Ankur Saxena, and Anupama Avasthi. "Prediction of cardiovascular diseases using random forest and naive Bayes algorithm." In Artificial Intelligence and Computational Dynamics for Biomedical Research, 21–38. De Gruyter, 2022. http://dx.doi.org/10.1515/9783110762044-002.
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Rekha G, Shanthini B, and Ranjith Kumar V. "Coronary Illness Prediction Using Random Forest Classifier." In Recent Trends in Intensive Computing. IOS Press, 2021. http://dx.doi.org/10.3233/apc210285.
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Heart diseases or Cardiovascular Diseases (CVDs) are the main cause of death on the planet throughout the most recent years and become the most dangerous disease in India and the entire world. The UCI repository is utilized to calculate the exactness of the AI calculations for foreseeing coronary illness, as k-nearest neighbor, decision tree, linear regression, and support vector machine. Different indications like chest pain, fasting of heartbeat, etc., are referenced. Large datasets, which are not available in medical and clinical research, are required in order to apply deep learning techniques. Surrogate data is generated from Cleveland dataset. The predicted results show that there is an improvement in classification accuracy. Heart disease is one of the most challenging diseases to diagnose as it is the most recognized killer in the present day. Utilizing AI algorithms, this paper gives anticipating coronary illness. Here, we will use the various machine learning algorithms such as Support Vector Machine, Random Forest, KNN, Naive Bayes, Decision Tree and LR.
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Metsker, Oleg, and Georgy Kopanitsa. "Influence of Healthcare Organization Factors on Cardiovascular Diseases Mortality." In Applying the FAIR Principles to Accelerate Health Research in Europe in the Post COVID-19 Era. IOS Press, 2021. http://dx.doi.org/10.3233/shti210835.
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One serious pandemic can nullify years of efforts to extend life expectancy and reduce disability. The coronavirus pandemic has been a perturbing factor that has provided an opportunity to assess not only the effectiveness of health systems for cardio-vascular diseases (CVD), but also their sustainability. The goal of our research is to analyze the influence of public health factors on the mortality from circulatory diseases using machine learning methods. We analysed a very large dataset that consisted of the information collected from the national registers in Russia. We included data from 2015 to 2021. It included 340 factors that characterize organization of healthcare in Russia. The resulting area under receiver operating characteristic curve (AUC of ROC) of the Random Forest based regression model was 92% with a testing dataset. The models allow for automated retraining as time passes and epidemiological and other situations change. They also allow additional characteristics of regions and health care organizations to be added to existing training datasets depending on the target. The developed models allow the calculation of the probability of the target for 6–12 months with an error of 8%. Moreover, the models allow to calculate scenarios and the value of the target indicator when other indicators of the region change.
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Yekkala, Indu, and Sunanda Dixit. "Prediction of Heart Disease Using Random Forest and Rough Set Based Feature Selection." In Coronary and Cardiothoracic Critical Care, 208–19. IGI Global, 2019. http://dx.doi.org/10.4018/978-1-5225-8185-7.ch011.
Full textAbstract:
Data is generated by the medical industry. Often this data is of very complex nature—electronic records, handwritten scripts, etc.—since it is generated from multiple sources. Due to the Complexity and sheer volume of this data necessitates techniques that can extract insight from this data in a quick and efficient way. These insights not only diagnose the diseases but also predict and can prevent disease. One such use of these techniques is cardiovascular diseases. Heart disease or coronary artery disease (CAD) is one of the major causes of death all over the world. Comprehensive research using single data mining techniques have not resulted in an acceptable accuracy. Further research is being carried out on the effectiveness of hybridizing more than one technique for increasing accuracy in the diagnosis of heart disease. In this article, the authors worked on heart stalog dataset collected from the UCI repository, used the Random Forest algorithm and Feature Selection using rough sets to accurately predict the occurrence of heart disease
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Pandey, Stuti, and Abhay Kumar Agarwal. "Comparison of Machine Learning Algorithms for Cardiovascular Disease Prediction." In Computational Methodologies for Electrical and Electronics Engineers, 111–26. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-3327-7.ch009.
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Cardiovascular disease prediction is a research field of healthcare which depends on a large volume of data for making effective and accurate predictions. These predictions can be more effective and accurate when used with machine learning algorithms because it can disclose all the concealed facts which are helpful in making decisions. The processing capabilities of machine learning algorithms are also very fast which is almost infeasible for human beings. Therefore, the work presented in this research focuses on identifying the best machine learning algorithm by comparing their performances for predicting cardiovascular diseases in a reasonable time. The machine learning algorithms which have been used in the presented work are naïve Bayes, support vector machine, k-nearest neighbors, and random forest. The dataset which has been utilized for this comparison is taken from the University of California, Irvine (UCI) machine learning repository named “Heart Disease Data Set.”
Conference papers on the topic "Random Forest, Questionnaire, Cardiovascular diseases"
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Aslam, Mohamed, and Jaisharma K. "Hierarchical Random Forest Formation with Nonlinear Regression Model for Cardiovascular Diseases Prediction." In 2021 International Conference on Computer Communication and Informatics (ICCCI). IEEE, 2021. http://dx.doi.org/10.1109/iccci50826.2021.9402571.
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