Open Access

ARTICLE

Probability Based Regression Analysis for the Prediction of Cardiovascular Diseases

Wasif Akbar¹, Adbul Mannan², Qaisar Shaheen^3,*, Mohammad Hijji⁴, Muhammad Anwar⁵, Muhammad Ayaz⁶

1 Department of Computer Science, NFC Institute of Engineering and Technology, Multan, Pakistan
2 Faculty of Computer Science & Information Technology, University of Malaya, Kuala Lumpur, Malaysia
3 Department of Computer Science and Information Technology, The Islamia University of Bahawalpur, Rahim Yar Khan Campus, Punjab, Pakistan
4 Faculty of Computers and Information Technology, University of Tabuk, Tabuk, 47512, Saudi Arabia
5 Department of Information Sciences, Division of Science and Technology, University of Education, Lahore, Pakistan
6 Sensor Networks and Cellular Systems (SNCS) research center, University of Tabuk, Tabuk, 47512, Saudi Arabia

* Corresponding Author: Qaisar Shaheen. Email:

Computers, Materials & Continua 2023, 75(3), 6269-6286. https://doi.org/10.32604/cmc.2023.036141

Received 18 September 2022; Accepted 29 January 2023; Issue published 29 April 2023

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.

---

Keywords

---