Probability Based Regression Analysis for the Prediction of Cardiovascular Diseases
Document Type
Article
Publication Date
1-1-2023
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
Machine learning, heart disease, cardiac disease, deep regression, regression learning
Divisions
fsktm
Publication Title
CMC-Computers Materials & Continua
Volume
75
Issue
3
Publisher
Tech Science Press
Publisher Location
871 CORONADO CENTER DR, SUTE 200, HENDERSON, NV 89052 USA