Abdellatif, Abdallah and Abdellatef, Hamdan and Kanesan, Jeevan and Chow, Chee-Onn and Chuah, Joon Huang and Gheni, Hassan Muwafaq (2022) Improving the heart disease detection and patients' survival using supervised infinite feature selection and improved weighted random forest. IEEE Access, 10. pp. 67363-67372. ISSN 2169-3536, DOI https://doi.org/10.1109/ACCESS.2022.3185129.
Full text not available from this repository.Abstract
Heart disease is the leading cause of death worldwide. A Machine Learning (ML) system can detect heart disease in the early stages to mitigate mortality rates based on clinical data. However, the class imbalance and high dimensionality issues have been a persistent challenge in ML, preventing accurate predictive data analysis in many real-world applications, including heart disease detection. In this regard, this work proposes a new method to address these issues and improve the predict the presence of heart disease and patients' survival, including supervised infinite feature selection (Inf-FSs) to find the most significant features and Improved Weighted Random Forest (IWRF) to predict heart disease, and Bayesian optimization to tune the new hyperparameters for IWRF. Two public datasets, including Statlog and heart disease clinical records, were used to develop and validate the proposed model. The proposed model is compared with other hybrid models to show its superiority using performance metrics like accuracy and f-measure to evaluate the models' performance. The results have shown that the proposed Inf-FSs-IWRF achieved better results than other models in attaining higher accuracy and F-measure on both datasets. Additionally, a comparative study has been performed to compare with previous studies, where the proposed model outperformed the others by an accuracy improvement of 2.4% and 4.6% on both datasets, respectively.
| Item Type: | Article |
|---|---|
| Funders: | Ministry of Higher Education through the Fundamental Research Grant Scheme (Grant No: RGS/1/2020/ICT02/UM/02/2) |
| Uncontrolled Keywords: | Heart; Random forests; Diseases; Feature extraction; Training; Radio frequency; Classification tree analysis; CVD detection; Heart disease classification; Feature selection; random forest; Imbalance; Bayesian optimization |
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science R Medicine > RA Public aspects of medicine > RA0421 Public health. Hygiene. Preventive Medicine |
| Divisions: | Faculty of Engineering > Department of Electrical Engineering |
| Depositing User: | Ms. Juhaida Abd Rahim |
| Date Deposited: | 17 Oct 2023 09:41 |
| Last Modified: | 17 Oct 2023 09:41 |
| URI: | http://eprints.um.edu.my/id/eprint/42085 |
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