Machine Learning and Feature Selection-Enabled Optimized Technique for Heart Disease Classification and Prediction
When dealing with a group of patients seeking treatment for heart-related diseases, doctors who specialize in the diagnosis and treatment of heart-related disorders have a difficult but critical task. It comes as no surprise that cardiovascular disease is a leading source of morbidity and death in...
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| Format: | Article |
| Language: | English |
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Institute of Fundamental Technological Research Polish Academy of Sciences
2024-08-01
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| Series: | Computer Assisted Methods in Engineering and Science |
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| Online Access: | https://cames.ippt.pan.pl/index.php/cames/article/view/602 |
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| author | P. Nancy Prasad Raghunath Mutkule Kalpana Sunil Thakre Ajay S. Ladkat S.B.G. Tilak Babu Sunil L. Bangare Mohd Naved |
| author_facet | P. Nancy Prasad Raghunath Mutkule Kalpana Sunil Thakre Ajay S. Ladkat S.B.G. Tilak Babu Sunil L. Bangare Mohd Naved |
| author_sort | P. Nancy |
| collection | DOAJ |
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When dealing with a group of patients seeking treatment for heart-related diseases, doctors who specialize in the diagnosis and treatment of heart-related disorders have a difficult but critical task. It comes as no surprise that cardiovascular disease is a leading source of morbidity and death in contemporary society. An expert system with clear categorization that may assist medical professionals in identifying heart disease condition based on the clinical data of a patient is often required by physicians. The aim of this work is to provide a method for the prediction and classification of cardiac disease based on machine learning and feature selection. The correlation-based feature selection (CFS) method is applied to the input data set in order to extract relevant features for analysis. The support vector machine with radial basis function (SVM RBF) and random forest algorithms are used here for data classification. Cleveland heart disease dataset is used in the experiment work. This dataset has 303 instances and 14 attributes. The accuracy, specificity and sensitivity of SVM RBF are higher than those of the random forest algorithm.
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| format | Article |
| id | doaj-art-5fbbfe3de602487eb80ebf81fd02f450 |
| institution | Kabale University |
| issn | 2299-3649 2956-5839 |
| language | English |
| publishDate | 2024-08-01 |
| publisher | Institute of Fundamental Technological Research Polish Academy of Sciences |
| record_format | Article |
| series | Computer Assisted Methods in Engineering and Science |
| spelling | doaj-art-5fbbfe3de602487eb80ebf81fd02f4502025-08-20T03:28:47ZengInstitute of Fundamental Technological Research Polish Academy of SciencesComputer Assisted Methods in Engineering and Science2299-36492956-58392024-08-0131410.24423/cames.2024.602Machine Learning and Feature Selection-Enabled Optimized Technique for Heart Disease Classification and PredictionP. Nancy0Prasad Raghunath Mutkule1Kalpana Sunil Thakre2Ajay S. Ladkat3S.B.G. Tilak Babu4Sunil L. Bangare5Mohd Naved6Department of Computing Technologies, School of Computing, SRM Institute of Science and Technology, Kattangulathur Campus, ChennaiDepartment of Information Technology, Sanjivani College of Engineering, Kopargaon, MaharashtraDepartment of Computer Engineering, MMCOE, Savitribai Phule Pune University, PuneDepartment of Instrumentation Engineering, Vishwakarma Institute of Technology, PuneAditya Engineering College, SurampalemDepartment of Information Technology, Sinhgad Academy of Engineering, Savitribai Phule Pune University, PuneJaipuria Institute of Management, Noida When dealing with a group of patients seeking treatment for heart-related diseases, doctors who specialize in the diagnosis and treatment of heart-related disorders have a difficult but critical task. It comes as no surprise that cardiovascular disease is a leading source of morbidity and death in contemporary society. An expert system with clear categorization that may assist medical professionals in identifying heart disease condition based on the clinical data of a patient is often required by physicians. The aim of this work is to provide a method for the prediction and classification of cardiac disease based on machine learning and feature selection. The correlation-based feature selection (CFS) method is applied to the input data set in order to extract relevant features for analysis. The support vector machine with radial basis function (SVM RBF) and random forest algorithms are used here for data classification. Cleveland heart disease dataset is used in the experiment work. This dataset has 303 instances and 14 attributes. The accuracy, specificity and sensitivity of SVM RBF are higher than those of the random forest algorithm. https://cames.ippt.pan.pl/index.php/cames/article/view/602machine learningheart disease predictionaccuracySVM RBFCFS feature selection |
| spellingShingle | P. Nancy Prasad Raghunath Mutkule Kalpana Sunil Thakre Ajay S. Ladkat S.B.G. Tilak Babu Sunil L. Bangare Mohd Naved Machine Learning and Feature Selection-Enabled Optimized Technique for Heart Disease Classification and Prediction Computer Assisted Methods in Engineering and Science machine learning heart disease prediction accuracy SVM RBF CFS feature selection |
| title | Machine Learning and Feature Selection-Enabled Optimized Technique for Heart Disease Classification and Prediction |
| title_full | Machine Learning and Feature Selection-Enabled Optimized Technique for Heart Disease Classification and Prediction |
| title_fullStr | Machine Learning and Feature Selection-Enabled Optimized Technique for Heart Disease Classification and Prediction |
| title_full_unstemmed | Machine Learning and Feature Selection-Enabled Optimized Technique for Heart Disease Classification and Prediction |
| title_short | Machine Learning and Feature Selection-Enabled Optimized Technique for Heart Disease Classification and Prediction |
| title_sort | machine learning and feature selection enabled optimized technique for heart disease classification and prediction |
| topic | machine learning heart disease prediction accuracy SVM RBF CFS feature selection |
| url | https://cames.ippt.pan.pl/index.php/cames/article/view/602 |
| work_keys_str_mv | AT pnancy machinelearningandfeatureselectionenabledoptimizedtechniqueforheartdiseaseclassificationandprediction AT prasadraghunathmutkule machinelearningandfeatureselectionenabledoptimizedtechniqueforheartdiseaseclassificationandprediction AT kalpanasunilthakre machinelearningandfeatureselectionenabledoptimizedtechniqueforheartdiseaseclassificationandprediction AT ajaysladkat machinelearningandfeatureselectionenabledoptimizedtechniqueforheartdiseaseclassificationandprediction AT sbgtilakbabu machinelearningandfeatureselectionenabledoptimizedtechniqueforheartdiseaseclassificationandprediction AT sunillbangare machinelearningandfeatureselectionenabledoptimizedtechniqueforheartdiseaseclassificationandprediction AT mohdnaved machinelearningandfeatureselectionenabledoptimizedtechniqueforheartdiseaseclassificationandprediction |