Implementation of a Heart Disease Risk Prediction Model Using Machine Learning
- Title
- Implementation of a Heart Disease Risk Prediction Model Using Machine Learning
- Creator
- Karthick K.; Aruna S.K.; Samikannu R.; Kuppusamy R.; Teekaraman Y.; Thelkar A.R.
- Description
- Cardiovascular disease prediction aids practitioners in making more accurate health decisions for their patients. Early detection can aid people in making lifestyle changes and, if necessary, ensuring effective medical care. Machine learning (ML) is a plausible option for reducing and understanding heart symptoms of disease. The chi-square statistical test is performed to select specific attributes from the Cleveland heart disease (HD) dataset. Support vector machine (SVM), Gaussian Naive Bayes, logistic regression, LightGBM, XGBoost, and random forest algorithm have been employed for developing heart disease risk prediction model and obtained the accuracy as 80.32%, 78.68%, 80.32%, 77.04%, 73.77%, and 88.5%, respectively. The data visualization has been generated to illustrate the relationship between the features. According to the findings of the experiments, the random forest algorithm achieves 88.5% accuracy during validation for 303 data instances with 13 selected features of the Cleveland HD dataset. 2022 K. Karthick et al.
- Source
- Computational and Mathematical Methods in Medicine, Vol-2022
- Date
- 2022-01-01
- Publisher
- Hindawi Limited
- Coverage
- Karthick K., Department Of Electrical And Electronics Engineering, Gmr Institute Of Technology, Andhra Pradesh, Rajam, India; Aruna S.K., Department Of Computer Science And Engineering, School Of Engineering And Technology, Christ (Deemed To Be University), Karnataka, Bangalore, India; Samikannu R., Department Of Electrical Computer And Telecommunications Engineering, Botswana International University Of Science And Technology, Palapye, Botswana; Kuppusamy R., Department Of Electrical And Electronics Engineering, Sri Sairam College Of Engineering, Bangalore, India; Teekaraman Y., Department Of Electronic And Electrical Engineering, The University Of Sheffield, Sheffield, S1 3JD, United Kingdom; Thelkar A.R., Faculty Of Electrical & Computer Engineering, Jimma Institute Of Technology, Jimma University, Ethiopia
- Rights
- All Open Access; Gold Open Access; Green Open Access
- Relation
- ISSN: 1748670X; PubMed ID: 35547562
- Format
- Online
- Language
- English
- Type
- Article
Collection
Citation
Karthick K.; Aruna S.K.; Samikannu R.; Kuppusamy R.; Teekaraman Y.; Thelkar A.R., “Implementation of a Heart Disease Risk Prediction Model Using Machine Learning,” CHRIST (Deemed To Be University) Institutional Repository, accessed February 28, 2025, https://archives.christuniversity.in/items/show/15401.