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Aprianto, Kasiful. "Heart Disease UCI Machine Learning." JITCE (Journal of Information Technology and Computer Engineering) 5, no. 02 (September 30, 2021): 70–74. http://dx.doi.org/10.25077/jitce.5.02.70-74.2021.
Повний текст джерела
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Mohammad, Ahmad Saeed, Musab T. S. Al-Kaltakchi, Jabir Alshehabi Al-Ani, and Jonathon A. Chambers. "Comprehensive Evaluations of Student Performance Estimation via Machine Learning." Mathematics 11, no. 14 (July 18, 2023): 3153. http://dx.doi.org/10.3390/math11143153.
Повний текст джерелаАнотація:
Success in student learning is the primary aim of the educational system. Artificial intelligence utilizes data and machine learning to achieve excellence in student learning. In this paper, we exploit several machine learning techniques to estimate early student performance. Two main simulations are used for the evaluation. The first simulation used the Traditional Machine Learning Classifiers (TMLCs) applied to the House dataset, and they are Gaussian Naïve Bayes (GNB), Support Vector Machine (SVM), Decision Tree (DT), Multi-Layer Perceptron (MLP), Random Forest (RF), Linear Discriminant Analysis (LDA), and Quadratic Discriminant Analysis (QDA). The best results were achieved with the MLP classifier with a division of 80% training and 20% testing, with an accuracy of 88.89%. The fusion of these seven classifiers was also applied and the highest result was equal to the MLP. Moreover, in the second simulation, the Convolutional Neural Network (CNN) was utilized and evaluated on five main datasets, namely, House, Western Ontario University (WOU), Experience Application Programming Interface (XAPI), University of California-Irvine (UCI), and Analytics Vidhya (AV). The UCI dataset was subdivided into three datasets, namely, UCI-Math, UCI-Por, and UCI-Fused. Moreover, the AV dataset has three targets which are Math, Reading, and Writing. The best accuracy results were achieved at 97.5%, 99.55%, 98.57%, 99.28%, 99.40%, 99.67%, 92.93%, 96.99%, and 96.84% for the House, WOU, XAPI, UCI-Math, UCI-Por, UCI-Fused, AV-Math, AV-Reading, and AV-Writing datasets, respectively, under the same protocol of evaluation. The system demonstrates that the proposed CNN-based method surpasses all seven conventional methods and other state-of-the-art-work.
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TURAN, SELIN CEREN, and MEHMET ALI CENGIZ. "ENSEMBLE LEARNING ALGORITHMS." Journal of Science and Arts 22, no. 2 (June 30, 2022): 459–70. http://dx.doi.org/10.46939/j.sci.arts-22.2-a18.
Повний текст джерелаАнотація:
Artificial intelligence is a method that is increasingly becoming widespread in all areas of life and enables machines to imitate human behavior. Machine learning is a subset of artificial intelligence techniques that use statistical methods to enable machines to evolve with experience. As a result of the advancement of technology and developments in the world of science, the interest and need for machine learning is increasing day by day. Human beings use machine learning techniques in their daily life without realizing it. In this study, ensemble learning algorithms, one of the machine learning techniques, are mentioned. The methods used in this study are Bagging and Adaboost algorithms which are from Ensemble Learning Algorithms. The main purpose of this study is to find the best performing classifier with the Classification and Regression Trees (CART) basic classifier on three different data sets taken from the UCI machine learning database and then to obtain the ensemble learning algorithms that can make this performance better and more determined using two different ensemble learning algorithms. For this purpose, the performance measures of the single basic classifier and the ensemble learning algorithms were compared
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Vranjković, Vuk S., Rastislav J. R. Struharik, and Ladislav A. Novak. "Reconfigurable Hardware for Machine Learning Applications." Journal of Circuits, Systems and Computers 24, no. 05 (April 8, 2015): 1550064. http://dx.doi.org/10.1142/s0218126615500644.
Повний текст джерелаАнотація:
This paper proposes universal coarse-grained reconfigurable computing architecture for hardware implementation of decision trees (DTs), artificial neural networks (ANNs), and support vector machines (SVMs), suitable for both field programmable gate arrays (FPGA) and application specific integrated circuits (ASICs) implementation. Using this universal architecture, two versions of DTs (functional DT and axis-parallel DT), two versions of SVMs (with polynomial and radial kernel) and two versions of ANNs (multi layer perceptron ANN and radial basis ANN) machine learning classifiers, have been implemented in FPGA. Experimental results, based on 18 benchmark datasets of standard UCI machine learning repository database, show that FPGA implementation provides significant improvement (1–2 orders of magnitude) in the average instance classification time, in comparison with software implementations based on R project.
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Kibria, Md Golam, and Mehmet Sevkli. "Application of Deep Learning for Credit Card Approval: A Comparison with Two Machine Learning Techniques." International Journal of Machine Learning and Computing 11, no. 4 (August 2021): 286–90. http://dx.doi.org/10.18178/ijmlc.2021.11.4.1049.
Повний текст джерелаАнотація:
The increased credit card defaulters have forced the companies to think carefully before the approval of credit applications. Credit card companies usually use their judgment to determine whether a credit card should be issued to the customer satisfying certain criteria. Some machine learning algorithms have also been used to support the decision. The main objective of this paper is to build a deep learning model based on the UCI (University of California, Irvine) data sets, which can support the credit card approval decision. Secondly, the performance of the built model is compared with the other two traditional machine learning algorithms: logistic regression (LR) and support vector machine (SVM). Our results show that the overall performance of our deep learning model is slightly better than that of the other two models.
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Anderies, Anderies, Jalaludin Ar Raniry William Tchin, Prambudi Herbowo Putro, Yudha Putra Darmawan, and Alexander Agung Santoso Gunawan. "Prediction of Heart Disease UCI Dataset Using Machine Learning Algorithms." Engineering, MAthematics and Computer Science (EMACS) Journal 4, no. 3 (September 30, 2022): 87–93. http://dx.doi.org/10.21512/emacsjournal.v4i3.8683.
Повний текст джерелаАнотація:
Heart disease is inflammation or damage to the heart and blood vessels over time. the disease can affect anyone of any age, gender, or social status. After many studies trying to overcome and learn about heart disease, in the end, this disease can be detected using machine learning systems. It predicts the likelihood of developing heart disease. The results of this system give the probability of heart disease as a percentage. Data collection using secret data mining. The data assets handled in python programming use two main algorithms for machine learning, the decision tree algorithm, and the Bayes naive algorithm which shows the best of both for heart disease accuracy. The results we get from this study show that the SVM algorithm is the algorithm with the most excellent precision. and the highest accuracy with a score of 85% in predicting heart disease using machine learning algorithms.Heart disease is inflammation or damage to the heart and blood vessels over time. the disease can affect anyone of any age, gender, or social status. After many studies trying to overcome and learn about heart disease, in the end, this disease can be detected using machine learning systems. It predicts the likelihood of developing heart disease. The results of this system give the probability of heart disease as a percentage. Data collection using secret data mining. The data assets handled in python programming use two main algorithms for machine learning, the decision tree algorithm, and the Bayes naive algorithm which shows the best of both for heart disease accuracy. The results we get from this study show that the SVM algorithm is the algorithm with the most excellent precision. and the highest accuracy with a score of 85% in predicting heart disease using machine learning algorithms.
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Verma, Raunak, Shashank Tandon, and Mr Vinayak. "Heart Disease Prediction using Machine Learning." International Journal for Research in Applied Science and Engineering Technology 10, no. 5 (May 31, 2022): 1872–76. http://dx.doi.org/10.22214/ijraset.2022.42687.
Повний текст джерелаАнотація:
Abstract: The term "heart disease" refers to any heart disease or condition that can cause heart problems. Cardiovascular disease (CVD) is the leading cause of death worldwide, taking many lives each year. CVD is a group of cardiovascular diseases and includes heart disease, cerebrovascular disease, rheumatic heart disease and other conditions. According to the World Health Organization (WHO), more than 17.9 million people worldwide die each year from coronary heart disease. If we take the example of India, every year the number of deaths due to heart disease has increased. Studies show that, from 2014 to 2019 the number of deaths from heart disease increased by 53%. Many threatening factors such as personal and work habits and genetic predisposition are major causes of heart disease. A variety of harmful habits such as smoking, alcohol and caffeine overdose, stress, and inactivity as well as other physical factors such as obesity, high blood pressure, high blood cholesterol, and pre-existing heart conditions are the main causes of heart disease. Over time, these harmful substances cause changes in the heart and blood vessels that can lead to heart attacks and strokes. Therefore, prevention of heart disease is very important to prevent these dangerous events and other potential complications of heart disease. Machine learning is a flexible part of AI that helps predict heart disease. In this research work, we will use the UCI database with 14 attributes to predict heart disease. The main goal of this study is to use ML algorithms to improve the heart disease prediction system and to more accurately predict these diseases in patients, thereby reducing the number of deaths by alerting patients. Keywords: Heart Diseases, Classification Algorithms, Machine Learning, UCI dataset.
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Hamed, Samer, Abdelwadood Mesleh, and Abdullah Arabiyyat. "Breast Cancer Detection Using Machine Learning Algorithms." International Journal of Computer Science and Mobile Computing 10, no. 11 (November 30, 2021): 4–11. http://dx.doi.org/10.47760/ijcsmc.2021.v10i11.002.
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This paper presents a computer-aided design (CAD) system that detects breast cancers (BCs). BC detection uses random forest, AdaBoost, logistic regression, decision trees, naïve Bayes and conventional neural networks (CNNs) classifiers, these machine learning (ML) based algorithms are trained to predicting BCs (malignant or benign) on BC Wisconsin data-set from the UCI repository, in which attribute clump thickness is used as evaluation class. The effectiveness of these ML algorithms are evaluated in terms of accuracy and F-measure; random forest outperformed the other classifiers and achieved 99% accuracy and 99% F-measure.
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Alnemari, Shouq, and Majid Alshammari. "Detecting Phishing Domains Using Machine Learning." Applied Sciences 13, no. 8 (April 7, 2023): 4649. http://dx.doi.org/10.3390/app13084649.
Повний текст джерелаАнотація:
Phishing is an online threat where an attacker impersonates an authentic and trustworthy organization to obtain sensitive information from a victim. One example of such is trolling, which has long been considered a problem. However, recent advances in phishing detection, such as machine learning-based methods, have assisted in combatting these attacks. Therefore, this paper develops and compares four models for investigating the efficiency of using machine learning to detect phishing domains. It also compares the most accurate model of the four with existing solutions in the literature. These models were developed using artificial neural networks (ANNs), support vector machines (SVMs), decision trees (DTs), and random forest (RF) techniques. Moreover, the uniform resource locator’s (URL’s) UCI phishing domains dataset is used as a benchmark to evaluate the models. Our findings show that the model based on the random forest technique is the most accurate of the other four techniques and outperforms other solutions in the literature.
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Suneetha Rani R, Gayathri B, Venkata Surya M, Jharani Asha Kiran P, and Siva Krishna R. "Detecting counterfeit banknotes with machine learning." South Asian Journal of Engineering and Technology 12, no. 3 (July 11, 2022): 146–51. http://dx.doi.org/10.26524/sajet.2022.12.40.
Повний текст джерелаАнотація:
The one important asset of our country is Bank currency and to create discrepancies of money miscreants introduce the fake notes which resembles to original note in thefinancial market. During demonetization time it is seen that so much of fake currency is floating in market. In general, by a human being, it is very difficult to identify forged note from the genuine not instead of various parameters designed for identification as many features of forged note are similar to original one. To discriminate between fake bank currency and original note is a challenging task. So, there must be an automated system that will be available in banks or in ATM machines. To design such an automated system there is need to design an efficient algorithm which is able to predict weather the banknote is genuine or forged bank currency as fake notes are designed with high precision. In this project six supervised machine learning algorithms are applied on dataset available on UCI machine learning repository for detection of Bank currency authentication
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Das, Shuvojit. "Human Activity Recognition using Machine Learning." International Journal for Research in Applied Science and Engineering Technology 10, no. 6 (June 30, 2022): 4188–93. http://dx.doi.org/10.22214/ijraset.2022.44722.
Повний текст джерелаАнотація:
Abstract: Nowadays, activity recognition is one of the most popular uses of machine learning algorithms. It's utilized in biomedical engineering, game production, and producing better metrics for sports training, among other things. Data from sensors linked to a person may be used to build supervised machine learning models that predict the activity that the person is doing. We will use data from the UCI Machine Learning Repository in this work. It contains data from the phone's accelerometer, gyroscope, and other sensors, which is used to build supervised prediction models using machine learning techniques like as SVM, Random Forest. This may be used to forecast the person's kind of movement, which is separated into six categories: walking, walking upstairs, walking downstairs, sitting, standing, and lying. We'll use a confusion matrix to compare the accuracy of different models.
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Li, Yibo, Chao Liu, Senyue Zhang, Wenan Tan, and Yanyan Ding. "Reproducing Polynomial Kernel Extreme Learning Machine." Journal of Advanced Computational Intelligence and Intelligent Informatics 21, no. 5 (September 20, 2017): 795–802. http://dx.doi.org/10.20965/jaciii.2017.p0795.
Повний текст джерелаАнотація:
Conventional kernel support vector machine (KSVM) has the problem of slow training speed, and single kernel extreme learning machine (KELM) also has some performance limitations, for which this paper proposes a new combined KELM model that build by the polynomial kernel and reproducing kernel on Sobolev Hilbert space. This model combines the advantages of global and local kernel function and has fast training speed. At the same time, an efficient optimization algorithm called cuckoo search algorithm is adopted to avoid blindness and inaccuracy in parameter selection. Experiments were performed on bi-spiral benchmark dataset, Banana dataset, as well as a number of classification and regression datasets from the UCI benchmark repository illustrate the feasibility of the proposed model. It achieves the better robustness and generalization performance when compared to other conventional KELM and KSVM, which demonstrates its effectiveness and usefulness.
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Muliawan, Agung, Achmad Rizal, and Sugondo Hadiyoso. "Heart Disease Prediction based on Physiological Parameters Using Ensemble Classifier and Parameter Optimization." Journal of Applied Engineering and Technological Science (JAETS) 5, no. 1 (December 10, 2023): 258–67. http://dx.doi.org/10.37385/jaets.v5i1.2169.
Повний текст джерелаАнотація:
This study describes the prediction of heart disease using ensemble classifiers with parameter optimization. As input, a public dataset was taken from UCI machine learning repository, which refers to the dataset at UCI Machine learning. The dataset consists of 13 variables that are considered to influence heart disease. Particle swarm optimization (PSO) was used for feature selection and principal component analysis (PCA) for feature extraction to reduce the features' dimensions. The application of parameter optimization on several machine learning methods such as SVM (Radial Basis Function), Deep learning, and Ensemble Classifier (bagging and boosting) to get the highest accuracy comparison. The results of this study using PSO dimensionality reduction in the public dataset of heart disease resulted in the slightest accuracy compared to PCA. In contrast, the highest accuracy was obtained from optimizing Deep Learning parameters with an accuracy of 84.47% and optimization of SVM RBF parameters with an accuracy of 83.56%. The highest accuracy in the ensemble classifier using bagging on SVM of 83.51%, with a difference of 0.5% from SVM without using bagging.
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Sandhiya, Prof Dr R. "Heat Disease Prediction using Machine Learning." International Journal for Research in Applied Science and Engineering Technology 9, no. VI (July 15, 2021): 846–52. http://dx.doi.org/10.22214/ijraset.2021.36372.
Повний текст джерелаАнотація:
In recent times, the diagnosis of heart disease has become a very critical task in the medical field. In the modern age, one person dies every minute due to heart disease. Data science has an important role in processing big amounts of data in the field of health sciences. Since the diagnosis of heart disease is a complex task, the assessment process should be automated to avoid the risks associated with it and alert the patient in advance. This paper uses the heart disease dataset available in the UCI Machine Learning Repository. The proposed work assesses the risk of heart disease in a patient by applying various data mining methods such as Naive Bayes, Decision Tree, KNN, Linear SVM, RBF SVM, Gaussian Process, Neural Network, Adabost, QDA and Random Forest. This paper provides a comparative study by analyzing the performance of various machine learning algorithms. Test results confirm that the KNN algorithm achieved the highest 97% accuracy compared to other implemented ML algorithms.
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Ahamad, Ghulab Nabi, Shafiullah, Hira Fatima, Imdadullah, S. M. Zakariya, Mohamed Abbas, Mohammed S. Alqahtani, and Mohammed Usman. "Influence of Optimal Hyperparameters on the Performance of Machine Learning Algorithms for Predicting Heart Disease." Processes 11, no. 3 (March 1, 2023): 734. http://dx.doi.org/10.3390/pr11030734.
Повний текст джерелаАнотація:
One of the most difficult challenges in medicine is predicting heart disease at an early stage. In this study, six machine learning (ML) algorithms, viz., logistic regression, K-nearest neighbor, support vector machine, decision tree, random forest classifier, and extreme gradient boosting, were used to analyze two heart disease datasets. One dataset was UCI Kaggle Cleveland and the other was the comprehensive UCI Kaggle Cleveland, Hungary, Switzerland, and Long Beach V. The performance results of the machine learning techniques were obtained. The support vector machine with tuned hyperparameters achieved the highest testing accuracy of 87.91% for dataset-I and the extreme gradient boosting classifier with tuned hyperparameters achieved the highest testing accuracy of 99.03% for the comprehensive dataset-II. The novelty of this work was the use of grid search cross-validation to enhance the performance in the form of training and testing. The ideal parameters for predicting heart disease were identified through experimental results. Comparative studies were also carried out with the existing studies focusing on the prediction of heart disease, where the approach used in this work significantly outperformed their results.
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Al Ahdal, Ahmed, Manik Rakhra, Rahul R. Rajendran, Farrukh Arslan, Moaiad Ahmad Khder, Binit Patel, Balaji Ramkumar Rajagopal, and Rituraj Jain. "Monitoring Cardiovascular Problems in Heart Patients Using Machine Learning." Journal of Healthcare Engineering 2023 (February 8, 2023): 1–15. http://dx.doi.org/10.1155/2023/9738123.
Повний текст джерелаАнотація:
The World Health Organization reports that heart disease is the most common cause of death globally, accounting for 17.9 million fatalities annually. The fundamentals of a cure, it is thought, are important symptoms and recognition of the illness. Traditional techniques are facing many challenges, ranging from delayed or unnecessary treatment to incorrect diagnoses, which can affect treatment progress, increase the bill, and give the disease more time to spread and harm the patient’s body. Such errors could be avoided and minimized by employing ML and AI techniques. Many significant efforts have been made in recent years to increase computer-aided diagnosis and detection applications, which is a rapidly growing area of research. Machine learning algorithms are especially important in CAD, which is used to detect patterns in medical data sources and make nontrivial predictions to assist doctors and clinicians in making timely decisions. This study aims to develop multiple methods for machine learning using the UCI set of data based on individuals’ medical attributes to aid in the early detection of cardiovascular disease. Various machine learning techniques are used to evaluate and review the results of the UCI machine learning heart disease dataset. The proposed algorithms had the highest accuracy, with the random forest classifier achieving 96.72% and the extreme gradient boost achieving 95.08%. This will assist the doctor in taking appropriate actions. The proposed technology will only be able to determine whether or not a person has a heart issue. The severity of heart disease cannot be determined using this method.
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Choudhary, Esha. "Spam SMS Prediction Using Machine Learning." International Journal for Research in Applied Science and Engineering Technology 11, no. 5 (May 31, 2023): 6868–76. http://dx.doi.org/10.22214/ijraset.2023.53235.
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Abstract: As the popularity of mobile phone devices has increased, Short Message Service (SMS) has grown into a multi-billion dollars industry. At the same time, reduction in the cost of messaging services has resulted in growth in unsolicited commercial advertisements (spams) being sent to mobile phones. In parts of Asia, up to 30% of text messages were spam in 2012. Lack of real databases for SMS spams, short length of messages and limited features, and their informal language are the factors that may cause the established email filtering algorithms to underperform in their classification. In this project, a dataset of real SMS Spams from UCI Machine Learning repository is used, and after pre-processing and vectorization, different machine learning algorithms are applied to the dataset. Finally, the results are compared and the best algorithm for spam filtering for text messaging is introduced and converted into an open-source website. The SMS spam collection set is used for testing the method. After collecting the various supervised learning algorithms, we find that the Multinomial Naïve Bayes algorithm gives us 97.1% Accuracy and 100% Precision
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Alaoui, Abdiya, and Zakaria Elberrichi. "Neuronal Communication Genetic Algorithm-Based Inductive Learning." Journal of Information Technology Research 13, no. 2 (April 2020): 141–54. http://dx.doi.org/10.4018/jitr.2020040109.
Повний текст джерелаАнотація:
The development of powerful learning strategies in the medical domain constitutes a real challenge. Machine learning algorithms are used to extract high-level knowledge from medical datasets. Rule-based machine learning algorithms are easily interpreted by humans. To build a robust rule-based algorithm, a new hybrid metaheuristic was proposed for the classification of medical datasets. The hybrid approach uses neural communication and genetic algorithm-based inductive learning to build a robust model for disease prediction. The resulting classification models are characterized by good predictive accuracy and relatively small size. The results on 16 well-known medical datasets from the UCI machine learning repository shows the efficiency of the proposed approach compared to other states-of-the-art approaches.
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Zhang, Senyue, and Wenan Tan. "An Extreme Learning Machine Based on the Mixed Kernel Function of Triangular Kernel and Generalized Hermite Dirichlet Kernel." Discrete Dynamics in Nature and Society 2016 (2016): 1–11. http://dx.doi.org/10.1155/2016/7293278.
Повний текст джерелаАнотація:
According to the characteristics that the kernel function of extreme learning machine (ELM) and its performance have a strong correlation, a novel extreme learning machine based on a generalized triangle Hermitian kernel function was proposed in this paper. First, the generalized triangle Hermitian kernel function was constructed by using the product of triangular kernel and generalized Hermite Dirichlet kernel, and the proposed kernel function was proved as a valid kernel function of extreme learning machine. Then, the learning methodology of the extreme learning machine based on the proposed kernel function was presented. The biggest advantage of the proposed kernel is its kernel parameter values only chosen in the natural numbers, which thus can greatly shorten the computational time of parameter optimization and retain more of its sample data structure information. Experiments were performed on a number of binary classification, multiclassification, and regression datasets from the UCI benchmark repository. The experiment results demonstrated that the robustness and generalization performance of the proposed method are outperformed compared to other extreme learning machines with different kernels. Furthermore, the learning speed of proposed method is faster than support vector machine (SVM) methods.
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Mohanty, Ashima Sindhu, Krishna Chandra Patra, and Priyadarsan Parida. "Toddler ASD Classification Using Machine Learning Techniques." International Journal of Online and Biomedical Engineering (iJOE) 17, no. 07 (July 2, 2021): 156. http://dx.doi.org/10.3991/ijoe.v17i07.23497.
Повний текст джерелаАнотація:
At present era, Autism Spectrum Disorder (ASD) has become one of the severe neurologically developed disorders throughout the world and early recognition can substantially get rid of this problem. The proposed work is based on the analysis of unbalanced ASD toddler dataset from UCI data repository. The work in this paper is performed in three stages. In first stage, the original data is preprocessed through converting the categorical attributes to numeric values by the process of frequency encoding followed by standardization of numeric attributes. In the second stage, the dimension of input is reduced using Principal component analysis (PCA). At the end, the classification of ASD Toddler data is performed through different machine learning classification models in two stages viz. through training parameter ε and through k-fold cross validation (k=10). The experimentation yields very high classification performance in comparison with other state-of-art approaches.
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Geldiev, Ertan Mustafa, Nayden Valkov Nenkov, and Mariana Mateeva Petrova. "EXERCISE OF MACHINE LEARNING USING SOME PYTHON TOOLS AND TECHNIQUES." CBU International Conference Proceedings 6 (September 25, 2018): 1062–70. http://dx.doi.org/10.12955/cbup.v6.1295.
Повний текст джерелаАнотація:
One of the goals of predictive analytics training using Python tools is to create a "Model" from classified examples that classifies new examples from a Dataset. The purpose of different strategies and experiments is to create a more accurate prediction model. The goals we set out in the study are to achieve successive steps to find an accurate model for a dataset and preserving it for its subsequent use using the python instruments. Once we have found the right model, we save it and load it later, to classify if we have "phishing" in our case. In the case that the path we reach to the discovery of the search model, we can ask ourselves how much we can automate everything and whether a computer program can be written to automatically go through the unified steps and to find the right model? Due to the fact that the steps for finding the exact model are often unified and repetitive for different types of data, we have offered a hypothetical algorithm that could write a complex computer program searching for a model, for example when we have a classification task. This algorithm is rather directional and does not claim to be all-encompassing. The research explores some features of Python Scientific Python Packages like Numpy, Pandas, Matplotlib, Scipy and scycit-learn to create a more accurate model. The Dataset used for the research was downloaded free from the UCI Machine Learning Repository (UCI Machine Learning Repository, 2017).
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Дюк, В. А., И. Г. Малыгин, and В. И. Прицкер. "Vehicle recognition by silhouettes – a three-stage machine learning method in computer vision systems." MORSKIE INTELLEKTUAL`NYE TEHNOLOGII)</msg>, no. 2(56) (June 9, 2022): 162–67. http://dx.doi.org/10.37220/mit.2022.56.2.022.
Повний текст джерелаАнотація:
В районах морских портов, на морских и сухопутных трассах актуальной является задача учета и контроля различных транспортных средств. Для решения этой задачи всё чаще используются технические системы распознавания таких средств, использующие видеокамеры. Однако видеоизображения по ряду причин не всегда бывают высокого качества. Поэтому теоретический и практический интерес представляет задача распознавания транспортных средств по сильно загрубленным их изображениям – силуэтам. В нашем исследовании используется экспериментальный материал из репозитория данных UCI (UCI Machine Learning Repository) и предлагается трехкаскадный метод машинного обучения для решения этой задачи. В первом каскаде формируется множество прецедентов классов – объектов с привязанными к ним собственными локальными контекстно-зависимыми метриками, обеспечивающими прецедентам максимально возможную «сферу действия». На втором каскаде применяются методы поиска логических правил (корректирующих логических правил), для которых описаниями (признаками) объектов служат расстояния от определенных в первом каскаде объектов в их собственных локальных пространствах. В третьем каскаде производится организация взаимодействия корректирующих логических правил путем их ансамблирования. Вероятность правильной классификации транспортных средств составила P = 0,972 (для оценки применялся метод 10-fold кросс-валидации). Этот результат превзошел ранее известные результаты. In the areas of seaports, on sea and land routes, the task of accounting and control of various vehicles is urgent. To solve this problem, technical recognition systems of such means using special sensors are increasingly used. However, video images are not always of high quality for a number of reasons. Therefore, the theoretical and practical interest is the problem of recognition of vehicles on their heavily coarsened images – silhouettes. Our study uses experimental material from the UCI (UCI Machine Learning Repository) data repository and proposes a three-stage machine learning method to solve this problem. In the first cascade, a set of class precedents is formed - objects with their own local context-dependent metrics attached to them, providing the precedents with the maximum possible “scope of action”. At the second cascade, methods of searching for logical rules (corrective logical rules) are applied, for which the descriptions (attributes) of objects are the distances from the objects defined in the first cascade in their own local spaces. In the third cascade, the interaction of corrective logical rules is organized by their ensemble. The probability of correctly classifying vehicles was P = 0.972 (a 10-fold cross-validation method was used for the assessment). This result surpassed previously known results.
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Subbulakshmi, C. V., and S. N. Deepa. "Medical Dataset Classification: A Machine Learning Paradigm Integrating Particle Swarm Optimization with Extreme Learning Machine Classifier." Scientific World Journal 2015 (2015): 1–12. http://dx.doi.org/10.1155/2015/418060.
Повний текст джерелаАнотація:
Medical data classification is a prime data mining problem being discussed about for a decade that has attracted several researchers around the world. Most classifiers are designed so as to learn from the data itself using a training process, because complete expert knowledge to determine classifier parameters is impracticable. This paper proposes a hybrid methodology based on machine learning paradigm. This paradigm integrates the successful exploration mechanism called self-regulated learning capability of the particle swarm optimization (PSO) algorithm with the extreme learning machine (ELM) classifier. As a recent off-line learning method, ELM is a single-hidden layer feedforward neural network (FFNN), proved to be an excellent classifier with large number of hidden layer neurons. In this research, PSO is used to determine the optimum set of parameters for the ELM, thus reducing the number of hidden layer neurons, and it further improves the network generalization performance. The proposed method is experimented on five benchmarked datasets of the UCI Machine Learning Repository for handling medical dataset classification. Simulation results show that the proposed approach is able to achieve good generalization performance, compared to the results of other classifiers.
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Bharti, Rohit, Aditya Khamparia, Mohammad Shabaz, Gaurav Dhiman, Sagar Pande, and Parneet Singh. "Prediction of Heart Disease Using a Combination of Machine Learning and Deep Learning." Computational Intelligence and Neuroscience 2021 (July 1, 2021): 1–11. http://dx.doi.org/10.1155/2021/8387680.
Повний текст джерелаАнотація:
The correct prediction of heart disease can prevent life threats, and incorrect prediction can prove to be fatal at the same time. In this paper different machine learning algorithms and deep learning are applied to compare the results and analysis of the UCI Machine Learning Heart Disease dataset. The dataset consists of 14 main attributes used for performing the analysis. Various promising results are achieved and are validated using accuracy and confusion matrix. The dataset consists of some irrelevant features which are handled using Isolation Forest, and data are also normalized for getting better results. And how this study can be combined with some multimedia technology like mobile devices is also discussed. Using deep learning approach, 94.2% accuracy was obtained.
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Křen, Tomáš, Martin Pilát, and Roman Neruda. "Automatic Creation of Machine Learning Workflows with Strongly Typed Genetic Programming." International Journal on Artificial Intelligence Tools 26, no. 05 (October 2017): 1760020. http://dx.doi.org/10.1142/s021821301760020x.
Повний текст джерелаАнотація:
Manual creation of machine learning ensembles is a hard and tedious task which requires an expert and a lot of time. In this work we describe a new version of the GP-ML algorithm which uses genetic programming to create machine learning workows (combinations of preprocessing, classification, and ensembles) automatically, using strongly typed genetic programming and asynchronous evolution. The current version improves the way in which the individuals in the genetic programming are created and allows for much larger workows. Additionally, we added new machine learning methods. The algorithm is compared to the grid search of the base methods and to its previous versions on a set of problems from the UCI machine learning repository.
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Majumder, Annwesha Banerjee, Somsubhra Gupta, and Dharmpal Singh. "Analysis and Observations of Associated Factors of Cardiovascular Disease." Journal of Physics: Conference Series 2286, no. 1 (July 1, 2022): 012024. http://dx.doi.org/10.1088/1742-6596/2286/1/012024.
Повний текст джерелаАнотація:
Abstract Machine learning contributes into gamut of domains starting from industry automation to healthcare services. It is a field of Artificial Intelligence using which machine can make decision without human intervention. There are many predominant machine learning algorithms which have proven their excellence in the field of regression and classification problem. Machine learning now a day is used in large scale in field of disease prediction. The acceptability of a machine learning based model depends on dataset used for training the model. Analysis of dataset is very important to identify the importance of individual attributes contribute to make decision. In this paper a cardiovascular disease dataset collected from UCI has been analyzed in detail to identify the distribution and impact of them in decision making.
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Nagarjuna Reddy, G., B. Dhana Lakshmi, C. Jaya Sree, A. Lokesh, and G. Madhuri. "AN EFFECTIVE MACHINE LEARNING APPRAOCH FOR CHRONIC KIDNEY DISEASE DETECTION." International Journal of Advanced Research 11, no. 04 (April 30, 2023): 616–23. http://dx.doi.org/10.21474/ijar01/16701.
Повний текст джерелаАнотація:
Chronic kidney disease (CKD) is a global health problem with high mortality and morbidity and mortality. Real-time performance using machine learning. In this study, we introduce machine learning for CKD diagnosis. CKD data is from the University of California, Irvine (UCI) Machine Learning Repository, which contains many missing values. KNN assignment selects multiple completed models with the best values ​​to predict missing data for each incomplete model and is used to load missing values.Although patients may ignore certain measures for a variety of reasons, missing data is often found in real clinical settings. After solving the missing data, models are constructed using machine learning algorithms (logistic regression, random forest, support vector machine, k-nearest neighbor, Naive Bayesian classifier, and feedforward neural network). Random forest machine learning models are the most accurate in this task.
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Chu, Yonghe, Hongfei Lin, Liang Yang, Yufeng Diao, Dongyu Zhang, Shaowu Zhang, Xiaochao Fan, Chen Shen, and Deqin Yan. "Globality-Locality Preserving Maximum Variance Extreme Learning Machine." Complexity 2019 (May 2, 2019): 1–18. http://dx.doi.org/10.1155/2019/1806314.
Повний текст джерелаАнотація:
An extreme learning machine (ELM) is a useful technique for machine learning; however, the existing extreme learning machine methods cannot exploit the geometric structure information or discriminate information of the data space well. Therefore, we propose a globality-locality preserving maximum variance extreme learning machine (GLELM) based on manifold learning. Based on the characteristics of the traditional ELM method, GLELM introduces the basic principles of linear discriminant analysis (LDA) and local preservation projection (LPP) into ELM, fully taking account of the discriminant information contained in the sample. This method can preserve the global and local manifold structures of data to optimize the projection direction of the classifier. Experiments on several widely used image databases and UCI datasets validate the performance of GLELM. The experimental results show that the proposed model achieves promising results compared to several state-of-the-art ELM algorithms.
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Amelia, Yutri. "PERBANDINGAN METODE MACHINE LEARNING UNTUK MENDETEKSI PENYAKIT JANTUNG." IDEALIS : InDonEsiA journaL Information System 6, no. 2 (July 15, 2023): 220–25. http://dx.doi.org/10.36080/idealis.v6i2.3043.
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Penyakit jantung termasuk ke dalam bagian penyakit kardiovaskular (CVD) atau sekelompok penyakit yang melibatkan pembuluh darah dan jantung yang merupakan salah satu penyakit serius yang diderita banyak orang secara global. Setiap tahunnya ada 17,9 juta jiwa yang meninggal akibat penyakit ini setiap tahunnya. Mendeteksi dini penyakit jantung sangat penting untuk perawatan dan pengobatan yang efektif. Penelitian ini memprediksi penyakit jantung dengan menggunakan metode Machine Learning (ML). ML memiliki efektifitas dan harga yang lebih murah untuk mendeteksi suatu penyakit. Penelitian ini bertujuan untuk memprediksi penyakit jantung dengan menggunakan perbandingan dari algoritma ML. Dalam penelitian ini menggunakan dataset dari UCI Machine Learning Repository. Pada penelitian ini, metode yang digunakan meliputi Random Forest, Support Vector Machine (SVM), XGBoost, K-Nearest Neighbor (KNN), Decision Tree, Logistic Regression serta Multi-Layer Perceptron Classifier (MLP). Dari penelitian ini didapatkan akurasi terbaik menggunakan algoritma XGBoost dengan akurasi mencapai 95,08%.
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Sreenivasa, N., Sudesh Pawaar, Shaurya Sparsh, and P. Ramesh Naidu. "Predicting the Kidney Diseases by Using Machine Learning Techniques." ITM Web of Conferences 57 (2023): 01011. http://dx.doi.org/10.1051/itmconf/20235701011.
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CKD (Chronic Kidney Diseases) is a persistent medical state categorized by the kidney damage that hinders their ability to effectively filter blood. Over time, this progressive disease can result in kidney failure. This project compares the performance of the Support Vectos Machines (SVM), logistic regression and Decision Tree algorithms for predicting the risk of CKD. In this project, the dataset utilized comprises a total of 25 attributes, consisting of 11 numerical features and 14 nominal features. In the training of machine learning algorithms for prediction, all 400 instances from the dataset are utilized. Among these instances, 250 are labeled as CKD cases, indicating the presence of chronic kidney disease, while the remaining 150 instances are categorized as non-CKD cases, denoting the absence of the condition. We utilized the UCI dataset, which underwent preprocessing to handle missing data. Using Python, we trained and built Support Vectors Machines (SVM), Logistic Regression, and Decision Tree models. The accuracy achieved with SVM was 97.3%, Logistic Regression was 93.8%, and Decision Tree yielded 95%, which are notable results.
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Gowri, J., R. Kamini, G. Vaishnavi, S. Thasvin, and C. Vaishna. "Heart Disease Prediction u sing Machine Learning." International Journal of Innovative Technology and Exploring Engineering 11, no. 8 (July 30, 2022): 29–32. http://dx.doi.org/10.35940/ijitee.h9148.0711822.
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Heart is one most important organ in our body. The prediction of heart disease is most complicated task in today world. There are number of instruments available in today’s worlds. These instruments are so expensive some of them can afford that instrumentals some of them cannot afford the instruments. Early prediction of heart disease will reduce the death rate. we can tell the patients before the hand. In todays world we all have the good amount of data using that good amount of data we can predict the heart disease using various machine learning techniques. The proposed method will tell to patients probabilities of heart diseases. In this paper using the UCI dataset performed various machine learning techniques like Logistic Regression, Decision tree, KNN, Naïve Bayes, Random Forest, XGBoost, Support vector machine . In this paper we used proposed methodology from PHASE I to PHASE VII Using the evaluation metrics we can check the performance of the machine learning which gives more accuracy from the above seven machine learning algorithm.
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B, Kavyashree, and Rakesh M D. "Prediction of Cardiac Arrhythmia using Machine Learning." International Journal for Research in Applied Science and Engineering Technology 10, no. 9 (September 30, 2022): 1698–706. http://dx.doi.org/10.22214/ijraset.2022.46900.
Повний текст джерелаАнотація:
Abstract: The Heart is one of the most important organ responsible for sustaining Human life. The Normal functioning of it is very important but the irregular functioning of it will causes few problems which may be classified as different heart disease. Arrhythmia an Irregular Heart Beat, which is considered as one of the Cardio Vascular Disease. Electrocardiogram (ECG) is the most preferred tool used to capture Heart Beat. Without taking proper pre-cautionary measures this may lead to sudden death, blood clots, heart failure, stroke, etc.. Machine learning is the study of computer algorithms. In this work by adopting Machine learning algorithms such as Logistic Regression, Decision Tree, SVM[Support Vector Machine]are done to foresee the Cardiac Arrhythmia. The data-sets are collected from UCI Repository & processed using python programming .From all the three applied algorithms the SVM model showed the better results of 91.41\% in terms of accuracy for 80/20 combinations of Train and Test data sets. Therefore from this work SVM model is considered as best algorithm for the prediction of Cardiac Arrhythmia.
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Ram Kumar, R. P., M. Sri Lakshmi, B. S. Ashwak, K. Rajeshwari, and S. Md Zaid. "Thyroid Disease Classification using Machine Learning Algorithms." E3S Web of Conferences 391 (2023): 01141. http://dx.doi.org/10.1051/e3sconf/202339101141.
Повний текст джерелаАнотація:
Thyroid gland is one of the body’s most important glands because it regulates the metabolism of the human body. It controls how the body works by releasing specific hormones into the blood. The two different hormone disorders are hypothyroidism and hyperthyroidism. When these disorders occur, the thyroid gland releases a particular hormone into the blood that regulates the metabolism of the body. Iodine deficiency, autoimmune conditions, and inflammation can contribute to thyroid issues. The disease is diagnosed using a blood test, but there is frequently some noise and disturbance. Techniques for cleaning data can be used to make it simple enough to perform analytics that show the patient's risk of developing thyroid disease. This paper deals with the analysis and classification models used in thyroid disease based on the information gathered from the dataset taken from the UCI machine learning repository. Machine learning plays a crucial role in the detection of thyroid disease. This paper suggests various machine-learning methods for thyroid detection and diagnosis for thyroid prevention.
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Kumar, Vikas, Vishal Kumar Yadav, and Er Sandeep Dubey. "Rainfall Prediction using Machine Learning." International Journal for Research in Applied Science and Engineering Technology 10, no. 5 (May 31, 2022): 2494–97. http://dx.doi.org/10.22214/ijraset.2022.42876.
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Abstract: In India, Agriculture contributes major role to Indian economy. For agriculture, Rainfall is important but during these days’ rainfall prediction has become a major challenging problem. Good prediction of rainfall provides knowledge and know in advance to take precautions and have better strategy about theirs crops. Global warming is also having severe effect on nature as well as mankind and it accelerates the change in climatic conditions. Because of its air is getting warmer and level of ocean is rising, leads to flood and cultivated field is changing into drought. Due to adverse climatic change leads to unseasonable and unreasonable amount of rainfall. To predict Rainfall is one of the best techniques to know about rainfall and climate. The main aim of this study revolves around providing correct climate description to the clients from various perspectives like agriculture, researchers, generation of power etc. to grasp the need of transformation in climate and its parameters like temperature, humidity, precipitation, wind speed that eventually directs to projection of rainfall. Rainfall also depends on geographic locations hence is an arduous task to predict. Machine Learning is the evolving subset of an AI, that helps in predicting the rainfall. In this research paper, we will be using UCI repository dataset with multiple attributes for predicting the rainfall. The main aim of this study is to develop the rainfall prediction system and predict the rainfall with better accuracy with the use of Machine Learning classification algorithms. Keywords: Rainfall Prediction system, Machine Learning, Dataset, Classification algorithms.
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Habib, Pranav, Uday Sharma, and Karman Singh Sethi. "Phishing Detection with Machine Learning." International Journal for Research in Applied Science and Engineering Technology 10, no. 12 (December 31, 2022): 1609–15. http://dx.doi.org/10.22214/ijraset.2022.48276.
Повний текст джерелаАнотація:
Abstract: The goal of our project is to implement a machine learning solution to the problem of detect- ing phishing and malicious web links. The end result of our project will be a software product which uses a machine learning algorithm to detect malicious URLs. Phishing is the technique of extracting user credentials and sensitive data from users by masquerading as a genuine website. In phishing, the user is provided with a mirror website which is identical to the legitimate one but with malicious code to extract and send user credentials to phishers. Phishing attacks can lead to huge financial losses for customers of banking and financial services. The traditional approach to phishing detection has been to either to use a blacklist of known phishing links or heuristically evaluate the attributes in a suspected phishing page to detect the presence of malicious codes. The heuristic function relies on trial and error to define the threshold, which is used to classify malicious links from benign ones. The drawback to this approach is poor accuracy and low adapt- ability to new phishing links. We plan to use machine learning to overcome these drawbacks by implementing some classification algorithms and comparing the performance of these algorithms on our dataset. We will test algorithms such as Logistic Regression, SVM, Decision Trees and Neural Networks on a dataset of phishing links from UCI Machine Learning repository and pick the best model to develop a browser plugin, which can be published as a browser extension.
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Sunita Chalageri, Prithvi Prakash, Rakshitha, and Rachanaa. "Predictive Analysis of Air Pollution using Machine Learning." ACS Journal for Science and Engineering 2, no. 1 (March 1, 2022): 16–32. http://dx.doi.org/10.34293/acsjse.v2i1.24.
Повний текст джерелаАнотація:
Where substances such as gases, particulates and biological molecules discharge hazardous or unsustainable quantities into the Earth's atmosphere, this is referred to as polluted air. It couldroot disease, allergy and smooth death in people; it may also impact on other living species, like animals and food crop, and harm the usual or constructed surroundings. Mutually human actions and normal processes can create air contamination. Air polution.
This study examines the limits of Linear Regression methods and the machine learning model's potential. Datasets are taken in the form of files from UCI, CSV (combination separated values) (University of California). Demonstrated through the comprehension of the explanatory variable in machine learning models that linear regression might help. This study reveals the character of the machine learning algorithms through research into different models' performance in connection with how they capture the link among air eminence and different variables.
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Hossain, M. Murad, Md Rana Ahmed, M. Zahid Hasan, M. Sultana, and K. Fatema. "Liable Characteristics Measure and Anticipate the Diabetes Disease Using Machine Learning Tools." European Journal of Statistics 3 (November 17, 2022): 2. http://dx.doi.org/10.28924/ada/stat.3.2.
Повний текст джерелаАнотація:
Diabetes is a cardiovascular disease. It is not only an epidemic in Bangladesh but also in the whole world that is increasing rapidly. At an early period of human life, machine learning techniques are used to predict diabetes datasets. In our research paper, we use the Pima diabetes dataset from the Kaggle UCI machine learning data repository. For diabetic patients and doctors, machine learning techniques are both cost-effective and time-saving. We apply KNN, Nave Bayes, Random forest, Support vector machine, Simple logistic, and J48 to Pima datasets. Besides these algorithms, we may develop an ensemble (Vote) hybrid model with WEKA software by combining individual methods that provide the best performance and accuracy. Also, try to make a comparison among all machine learning tool’s accuracy and performance with the proposed ensemble model.
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Kokol, Peter, Jan Jurman, Tajda Bogovič, Tadej Završnik, Jernej Završnik, and Helena Blažun Vošner. "Supporting Real World Decision Making in Coronary Diseases Using Machine Learning." INQUIRY: The Journal of Health Care Organization, Provision, and Financing 58 (January 2021): 004695802199733. http://dx.doi.org/10.1177/0046958021997338.
Повний текст джерелаАнотація:
Cardiovascular diseases are one of the leading global causes of death. Following the positive experiences with machine learning in medicine we performed a study in which we assessed how machine learning can support decision making regarding coronary artery diseases. While a plethora of studies reported high accuracy rates of machine learning algorithms (MLA) in medical applications, the majority of the studies used the cleansed medical data bases without the presence of the “real world noise.” Contrary, the aim of our study was to perform machine learning on the routinely collected Anonymous Cardiovascular Database (ACD), extracted directly from a hospital information system of the University Medical Centre Maribor). Many studies used tens of different machine learning approaches with substantially varying results regarding accuracy (ACU), hence they were not usable as a base to validate the results of our study. Thus, we decided, that our study will be performed in the 2 phases. During the first phase we trained the different MLAs on a comparable University of California Irvine UCI Heart Disease Dataset. The aim of this phase was first to define the “standard” ACU values and second to reduce the set of all MLAs to the most appropriate candidates to be used on the ACD, during the second phase. Seven MLAs were selected and the standard ACUs for the 2-class diagnosis were 0.85. Surprisingly, the same MLAs achieved the ACUs around 0.96 on the ACD. A general comparison of both databases revealed that different machine learning algorithms performance differ significantly. The accuracy on the ACD reached the highest levels using decision trees and neural networks while Liner regression and AdaBoost performed best in UCI database. This might indicate that decision trees based algorithms and neural networks are better in coping with real world not “noise free” clinical data and could successfully support decision making concerned with coronary diseasesmachine learning.
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Liu, Xiaobo, Guangjun Wang, Zhihua Cai, and Harry Zhang. "A MultiBoosting Based Transfer Learning Algorithm." Journal of Advanced Computational Intelligence and Intelligent Informatics 19, no. 3 (May 20, 2015): 381–88. http://dx.doi.org/10.20965/jaciii.2015.p0381.
Повний текст джерелаАнотація:
Ensemble learning is sophisticated machine learning use to solve many problems in practical applications. MultiBoosting, a cutting-edge learning approach in ensemble learning, is combined with AdaBoost and wagging. It retains AdaBoost’s bias reduction while adding wagging’s variance reduction to that already obtained by AdaBoost, thus reducing the total number of errors in classification. Data characteristics do not always follow traditional machine learning rules, however, so transfer learning acts to solve this problem. We propose a TrMultiBoosting algorithm, composed of MultiBoosting and state-of-the-art transfer learning algorithm TrAdaBoost for transfer learning. We use naive bayes as the basic learning algorithm. TrMultiBoosting has proven to present a decision committee with higher prediction accuracy on UCI data sets than either TrAdaBoost or MultiBoosting.
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Ayuningtyas, Puji, Rahmawati Rahmawati, and Akhmad Miftahusalam. "Comparison of Machine Learning and Deep Learning Algorithms for Classification of Breast Cancer." Journal of Computer Engineering, Electronics and Information Technology 2, no. 2 (October 1, 2023): 89–98. http://dx.doi.org/10.17509/coelite.v2i2.59717.
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Statistical data from the American Cancer Society which shows that breast cancer ranks first with the highest number of cases of all types of cases of malignant tumors (cancer) worldwide. through a data mining process that is used to extract information and data analysis, a classification process can be carried out to carry out further analysis of the pattern of a data. The dataset used in this study is the Breast Cancer Wisconsin (Diagnostic) Dataset obtained from UCI Machine Learning. The purpose of this study is to compare five algorithms, namely Logistic Regression, K Neighbors Classifier (KNN), Decision Tree Classifier, Deep Neural Network, Genetic Algorithm. The results showed that deep neural network algorithms and multilayer perceptron-genetic algorithms get 96% accuracy, logistic regression algorithms have 96% accuracy, then KNN with 94%, and decision tree classifier with 92%.
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Li, Dongtan. "Evaluating Various Machine Learning Techniques in Credit Risk Area." BCP Business & Management 38 (March 2, 2023): 2836–44. http://dx.doi.org/10.54691/bcpbm.v38i.4198.
Повний текст джерелаАнотація:
Implementing machine learning techniques to credit scoring is a popular method, which is widely used by many financial institutions and banks at present. As the fast development of machine learning tools, these technologies could provide people more accurate predictions and help enterprises avoid future risk. A supervised machine learning technique is utilized in this research as the classification approach. In this experiment, several machine learning algorithms will be compared in order to present the performance by evaluating the type of credit risk. The data is about assessing customers of a German banking systems from the UCI Machine Learning Repository, which contains 5000 instances and 21 attributes. The final result of this research shows the comparison of 12 scenarios among different combinations of balancing methods, feature selection methods, and predictive algorithms, which finally presents that the collection of Adaptive Synthetic, Boruta and k-Nearest Neighbor receives the highest accuracy score.
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V Mareeswari, Sunita Chalageri, and Kavita K Patil. "Predicting Chronic Kidney Disease Using KNN Algorithm." ACS Journal for Science and Engineering 1, no. 2 (September 9, 2021): 16–24. http://dx.doi.org/10.34293/acsjse.v1i2.10.
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Chronic kidney disease (CKD) is a world heath issues, and that also includes damages and can’t filter blood the way it should be. since we cannot predict the early stages of CKD, patience will fail to recognise the disease. Pre detection of CKD will allow patience to get timely facility to ameliorate the progress of the disease. Machine learning models will effectively aid clinician’s progress this goal because of the early and accurate recognition performances. The CKD data set is collected from the University of California Irvine (UCI) Machine Learning Recognition. Multiple Machine and deep learning algorithm used to predict the chronic kidney disease.
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Zhang, Zeliang. "Big data analysis with artificial intelligence technology based on machine learning algorithm." Journal of Intelligent & Fuzzy Systems 39, no. 5 (November 19, 2020): 6733–40. http://dx.doi.org/10.3233/jifs-191265.
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Artificial intelligence technology has been applied very well in big data analysis such as data classification. In this paper, the application of the support vector machine (SVM) method from machine learning in the problem of multi-classification was analyzed. In order to improve the classification performance, an improved one-to-one SVM multi-classification method was creatively designed by combining SVM with the K-nearest neighbor (KNN) method. Then the method was tested using UCI public data set, Statlog statistical data set and actual data. The results showed that the overall classification accuracy of the one-to-many SVM, one-to-one SVM and improved one-to-one SVM were 72.5%, 77.25% and 91.5% respectively in the classification of UCI publication data set and Statlog statistical data set, and the total classification accuracy of the neural network, decision tree, basic one-to-one SVM, directed acyclic graph improved one-to-one SVM and fuzzy decision method improved one-to-one SVM and improved one-to-one SVM proposed in this study was 83.98%, 84.55%, 74.07%, 81.5%, 82.68% and 92.9% respectively in the classification of fault data of transformer, which demonstrated the improved one-to-one SVM had good reliability. This study provides some theoretical bases for the application of methods such as machine learning in big data analysis.
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N C, Danushri. "Clinical Decision Making using Machine Learning." International Journal for Research in Applied Science and Engineering Technology 10, no. 7 (July 31, 2022): 3061–64. http://dx.doi.org/10.22214/ijraset.2022.45625.
Повний текст джерелаАнотація:
Abstract: Deaths due to cardiovascular diseases are increasing at an alarming rate. This led to nearly 2.1 million deaths in India in 2015. Heart disease is one of the deadliest causes of death worldwide and has a major impact on the lives of rural people. According to a recent study, cardiovascular disease mortality among rural Indians has surpassed urban Indians. Such numbers are alarming, especially when 68% of India's population lives in rural areas that have poor access to quality healthcare. This paper aims to provide a solution to this problem by introducing a new model*clinical*decision*support system*, abbreviated as CDSS, which*includes machine learning algorithms for the diagnosis of cardiovascular diseases. CDSS is intelligent enough to diagnose a patient's*disease* and help the doctor prescribe the*correct medication, reducing the cost and effort required to prescribe unnecessary treatment. *In this work, we applied correlation-based feature selection (CFS) and a multilayer perceptron classifier on a large heart disease dataset. The dataset used in this study is the "Cleveland Clinic Foundation Heart Disease Dataset" available at the UCI Machine Learning Repository. Our proposed model produced greater accuracy compared to other existing models used in this study. This system can be integrated into a public health care setting to help rural people get a correct, timely and cost-effective diagnosis.
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Jia, Xiao, Xiaolin Sun, and Xingang Zhang. "Breast Cancer Identification Using Machine Learning." Mathematical Problems in Engineering 2022 (October 3, 2022): 1–8. http://dx.doi.org/10.1155/2022/8122895.
Повний текст джерелаАнотація:
Breast cancer is a cancer disease that seriously threatens women’s health and occupies the first place in female cancer mortality. At present, the incidence rate of breast cancer in China is the first in the world and is on the rise. In view of the serious harm of breast cancer to life and health, researchers and institutions are making unremitting efforts to find a perfect diagnosis and treatment plan. With the improvement of computer performance and machine learning levels, intelligent algorithms have been able to replace human behavior and judgment in some fields. The traditional breast cancer diagnosis process requires medical experts to observe patient data repeatedly. In this case, the algorithm technology is used to quickly feedback a high probability reference result to doctors, which is particularly important to increase the diagnosis efficiency and reduce the burden of doctors. In order to improve the accuracy of existing breast cancer recognition methods, this paper proposes and implements a scheme based on a whale optimization algorithm to iteratively adjust the key parameters of the support vector machine to improve the accuracy of breast cancer recognition. In order to verify the performance of the WOA-SVM algorithm, this paper uses the Wisconsin breast cancer data in the UCI database for performance verification experiments. Experiments show that the WOA-SVM model has higher recognition accuracy than the traditional breast cancer recognition model.
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DIQI, MOHAMMAD, I. WAYAN ORDIYASA, and MARSELINA ENDAH HISWATI. "Comparative Analysis of Kidney Disease Detection Using Machine Learning." MATICS: Jurnal Ilmu Komputer dan Teknologi Informasi (Journal of Computer Science and Information Technology) 15, no. 2 (October 23, 2023): 58–62. http://dx.doi.org/10.18860/mat.v15i2.21468.
Повний текст джерелаАнотація:
This research aimed to compare the performance of ten machine learning algorithms for detecting kidney disease, utilizing data from UCI Machine Learning Repository. The algorithms tested included K-Nearest Neighbour, RBF SVM, Linear SVM, Neural Net, Decision Tree, Naïve Bayes, AdaBoost, Random Forest, Gaussian Process, and QDA. The evaluation metrics used were accuracy, precision, recall, and F1-score. The findings revealed that AdaBoost was the most effective algorithm for all evaluation metrics, achieving an accuracy, precision, recall, and F1-score of 1.00. Random Forest and RBF followed closely, while Naïve Bayes and QDA had the lowest performance. These results suggest that machine learning algorithms, especially ensemble methods such as AdaBoost, can significantly improve the accuracy and efficiency of detecting kidney disease. This can lead to better patient outcomes and reduced healthcare costs.
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Dadheech, Pankaj, Vijay Kalmani, Sanwta Ram Dogiwal, Vijay Kumar Sharma, Ankit Kumar, and Saroj Kumar Pandey. "Breast cancer prediction using supervised machine learning techniques." Journal of Information and Optimization Sciences 44, no. 3 (2023): 383–92. http://dx.doi.org/10.47974/jios-1348.
Повний текст джерелаАнотація:
Breast cancer is one of the most prevalent diseases in India’s urban regions and the second most common in the country’s rural parts. In India, a woman is diagnosed with breast cancer growth every four minutes, and a woman dies from breast cancer sickness every thirteen minutes. Over half of breast cancer patients in India are diagnosed with stage 3 or 4 illness, which has extremely low survival rates; hence, an urgent need exists for a rapid detection strategy. To forecast if a patient is at risk for breast cancer, we utilise the classification techniques of machine learning, in which the machine learning model learns from the previous information and can anticipate on the new information that is generated by the data. To create a model using Logistic Regression, Support Vector Machines, and Random Forests, this dataset was collected from the UCI repository and studied in this study. The primary goal is to improve the accuracy, precision, and sensitivity of all the algorithms that are used to categorise data in terms of the competency and viability of each and every algorithm. Random Forest has been shown to be the most accurate in classifying breast cancer, with a precision of 98.60 percent in tests. The Scientific Python Development Environment is used to carry out this machine learning study, which is written in the python programming language.
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M S*, Sruthi, Sushmitha Magudeswaren, Soniya Tamilarasu, and Sushmitha Muralitharen. "Diabetes Prediction and Analysis using Machine Learning Methods." International Journal of Innovative Technology and Exploring Engineering 9, no. 6 (April 30, 2020): 568–70. http://dx.doi.org/10.35940/ijitee.e2689.049620.
Повний текст джерелаАнотація:
Different computational procedures and gadgets are open for data examination. At the present time, took the advantages of those open developments to improve the adequacy of the estimate model for the desire for a Type-2 Diabetic Patient. We intend to inquire about how diabetes scenes are impacted by patients' characteristics and estimations. The capable gauge model is required for clinical researchers. Until generally, Type II diabetes was evaluated uncommon in children. The contamination is, nonetheless, creating among youths in peoples with high paces of Type II diabetes in adults. This work presents the adequacy of Gradient Boosted Classifier which is obscure in past current works. It is related to two AI figuring’s, for instance, Neural Networks, Random Forest. These estimations are applied to the Pima Indians Diabetes Database (PIDD) which is sourced from the UCI AI storage facility. The models made are surveyed by standard techniques, for instance, AUC, Recall, and Accuracy. As obvious, Gradient helped classifier clobbers other two classifiers in all introduction qualities.
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Gao, Hang, Xin-Wang Liu, Yu-Xing Peng, and Song-Lei Jian. "Sample-Based Extreme Learning Machine with Missing Data." Mathematical Problems in Engineering 2015 (2015): 1–11. http://dx.doi.org/10.1155/2015/145156.
Повний текст джерелаАнотація:
Extreme learning machine (ELM) has been extensively studied in machine learning community during the last few decades due to its high efficiency and the unification of classification, regression, and so forth. Though bearing such merits, existing ELM algorithms cannot efficiently handle the issue of missing data, which is relatively common in practical applications. The problem of missing data is commonly handled by imputation (i.e., replacing missing values with substituted values according to available information). However, imputation methods are not always effective. In this paper, we propose a sample-based learning framework to address this issue. Based on this framework, we develop two sample-based ELM algorithms for classification and regression, respectively. Comprehensive experiments have been conducted in synthetic data sets, UCI benchmark data sets, and a real world fingerprint image data set. As indicated, without introducing extra computational complexity, the proposed algorithms do more accurate and stable learning than other state-of-the-art ones, especially in the case of higher missing ratio.
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CN, Lakshmi, Bindhudhree M, Jaya Poojary, Manish C, and Shylaja B. "Heart Disease Prediction Using Machine Learning Algorithms." International Journal for Research in Applied Science and Engineering Technology 10, no. 7 (July 31, 2022): 1444–51. http://dx.doi.org/10.22214/ijraset.2022.44895.
Повний текст джерелаАнотація:
Abstract: Heart disease, alternatively known as cardiovascular disease, encases various conditions that impact the heart and is the primary basis of death worldwide over the span of the past few decades. It associates many risk factors in heart disease and a need of the time to get accurate, reliable, and sensible approaches to make an early diagnosis to achieve prompt management of the disease. Data mining is a commonly used technique for processing enormous data in the healthcare domain. Researchers apply several data mining and machine learning techniques to analyse huge complex medical data, helping healthcare professionals to predict heart disease. This research paper presents various attributes related to heart disease, and the model on basis of supervised learning algorithms as Naïve Bayes, decision tree, K-nearest neighbor, Support vector machine and random forest algorithm. It uses the existing dataset from the Cleveland database of UCI repository of heart disease patients. The dataset comprises 303 instances and 76 attributes. Of these 76 attributes, only 14 attributes are considered for testing, important to substantiate the performance of diferent algorithms. This research paper aims to envision the probability of developing heart disease in the patients.