Comparative Study on Heart Disease Prediction Using Feature Selection Techniques on Classification Algorithms
Heart disease is recognized as one of the leading factors of death rate worldwide. Biomedical instruments and various systems in hospitals have massive quantities of clinical data. Therefore, understanding the data related to heart disease is very important to improve prediction accuracy. This artic...
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Applied Computational Intelligence and Soft Computing |
| Online Access: | http://dx.doi.org/10.1155/2021/5581806 |
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| _version_ | 1832550591549669376 |
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| author | Kaushalya Dissanayake Md Gapar Md Johar |
| author_facet | Kaushalya Dissanayake Md Gapar Md Johar |
| author_sort | Kaushalya Dissanayake |
| collection | DOAJ |
| description | Heart disease is recognized as one of the leading factors of death rate worldwide. Biomedical instruments and various systems in hospitals have massive quantities of clinical data. Therefore, understanding the data related to heart disease is very important to improve prediction accuracy. This article has conducted an experimental evaluation of the performance of models created using classification algorithms and relevant features selected using various feature selection approaches. For results of the exploratory analysis, ten feature selection techniques, i.e., ANOVA, Chi-square, mutual information, ReliefF, forward feature selection, backward feature selection, exhaustive feature selection, recursive feature elimination, Lasso regression, and Ridge regression, and six classification approaches, i.e., decision tree, random forest, support vector machine, K-nearest neighbor, logistic regression, and Gaussian naive Bayes, have been applied to Cleveland heart disease dataset. The feature subset selected by the backward feature selection technique has achieved the highest classification accuracy of 88.52%, precision of 91.30%, sensitivity of 80.76%, and f-measure of 85.71% with the decision tree classifier. |
| format | Article |
| id | doaj-art-cd00bdeff7e144b2946f8135de359528 |
| institution | Kabale University |
| issn | 1687-9732 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Applied Computational Intelligence and Soft Computing |
| spelling | doaj-art-cd00bdeff7e144b2946f8135de3595282025-02-03T06:06:26ZengWileyApplied Computational Intelligence and Soft Computing1687-97322021-01-01202110.1155/2021/5581806Comparative Study on Heart Disease Prediction Using Feature Selection Techniques on Classification AlgorithmsKaushalya Dissanayake0Md Gapar Md Johar1School of Graduate StudiesInformation Technology Innovation CentreHeart disease is recognized as one of the leading factors of death rate worldwide. Biomedical instruments and various systems in hospitals have massive quantities of clinical data. Therefore, understanding the data related to heart disease is very important to improve prediction accuracy. This article has conducted an experimental evaluation of the performance of models created using classification algorithms and relevant features selected using various feature selection approaches. For results of the exploratory analysis, ten feature selection techniques, i.e., ANOVA, Chi-square, mutual information, ReliefF, forward feature selection, backward feature selection, exhaustive feature selection, recursive feature elimination, Lasso regression, and Ridge regression, and six classification approaches, i.e., decision tree, random forest, support vector machine, K-nearest neighbor, logistic regression, and Gaussian naive Bayes, have been applied to Cleveland heart disease dataset. The feature subset selected by the backward feature selection technique has achieved the highest classification accuracy of 88.52%, precision of 91.30%, sensitivity of 80.76%, and f-measure of 85.71% with the decision tree classifier.http://dx.doi.org/10.1155/2021/5581806 |
| spellingShingle | Kaushalya Dissanayake Md Gapar Md Johar Comparative Study on Heart Disease Prediction Using Feature Selection Techniques on Classification Algorithms Applied Computational Intelligence and Soft Computing |
| title | Comparative Study on Heart Disease Prediction Using Feature Selection Techniques on Classification Algorithms |
| title_full | Comparative Study on Heart Disease Prediction Using Feature Selection Techniques on Classification Algorithms |
| title_fullStr | Comparative Study on Heart Disease Prediction Using Feature Selection Techniques on Classification Algorithms |
| title_full_unstemmed | Comparative Study on Heart Disease Prediction Using Feature Selection Techniques on Classification Algorithms |
| title_short | Comparative Study on Heart Disease Prediction Using Feature Selection Techniques on Classification Algorithms |
| title_sort | comparative study on heart disease prediction using feature selection techniques on classification algorithms |
| url | http://dx.doi.org/10.1155/2021/5581806 |
| work_keys_str_mv | AT kaushalyadissanayake comparativestudyonheartdiseasepredictionusingfeatureselectiontechniquesonclassificationalgorithms AT mdgaparmdjohar comparativestudyonheartdiseasepredictionusingfeatureselectiontechniquesonclassificationalgorithms |