Akbar, Wasif and Mannan, Adbul and Shaheen, Qaisar and Hijji, Mohammad and Anwar, Muhammad and Ayaz, Muhammad (2023) Probability Based Regression Analysis for the Prediction of Cardiovascular Diseases. CMC-Computers Materials & Continua, 75 (3). pp. 6269-6286. ISSN 1546-2218, DOI https://doi.org/10.32604/cmc.2023.036141.
Full text not available from this repository.Abstract
Machine Learning (ML) has changed clinical diagnostic procedures drastically. Especially in Cardiovascular Diseases (CVD), the use of ML is indispensable to reducing human errors. Enormous studies focused on disease prediction but depending on multiple parameters, further investigations are required to upgrade the clinical procedures. Multi-layered implementation of ML also called Deep Learning (DL) has unfolded new horizons in the field of clinical diagnostics. DL formulates reliable accuracy with big datasets but the reverse is the case with small datasets. This paper proposed a novel method that deals with the issue of less data dimensionality. Inspired by the regression analysis, the proposed method classifies the data by going through three different stages. In the first stage, feature representation is converted into probabilities using multiple regression techniques, the second stage grasps the probability conclusions from the previous stage and the third stage fabricates the final classifications. Extensive experiments were carried out on the Cleveland heart disease dataset. The results show significant improvement in classification accuracy. It is evident from the comparative results of the paper that the prevailing statistical ML methods are no more stagnant disease prediction techniques in demand in the future.
| Item Type: | Article |
|---|---|
| Funders: | UNSPECIFIED |
| Uncontrolled Keywords: | Machine learning; heart disease; cardiac disease; deep regression; regression learning |
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science |
| Divisions: | Faculty of Computer Science & Information Technology |
| Depositing User: | Ms. Juhaida Abd Rahim |
| Date Deposited: | 12 Sep 2025 03:33 |
| Last Modified: | 12 Sep 2025 03:33 |
| URI: | http://eprints.um.edu.my/id/eprint/50469 |
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