Computational Learning Model for Prediction of Heart Disease Using Machine Learning Based on a New Regularizer

Abdulaziz Albahr; Marwan Albahar; Mohammed Thanoon; Muhammad Binsawad

doi:10.1155/2021/8628335

Computational Learning Model for Prediction of Heart Disease Using Machine Learning Based on a New Regularizer

Comput Intell Neurosci. 2021 Nov 11:2021:8628335. doi: 10.1155/2021/8628335. eCollection 2021.

Authors

Abdulaziz Albahr¹, Marwan Albahar², Mohammed Thanoon², Muhammad Binsawad³

Affiliations

¹ College of Applied Medical Sciences, King Saud Bin Abdulaziz University for Health Sciences, Al-Ahsa 31982, Saudi Arabia.
² Department of Science, Umm Al Qura University, P.O. Box 715, Mecca, Saudi Arabia.
³ King Abdulaziz University, Computer Information System Department, Jeddah, Saudi Arabia.

Abstract

Heart diseases are characterized as heterogeneous diseases comprising multiple subtypes. Early diagnosis and prognosis of heart disease are essential to facilitate the clinical management of patients. In this research, a new computational model for predicting early heart disease is proposed. The predictive model is embedded in a new regularization based on decaying the weights according to the weight matrices' standard deviation and comparing the results against its parents (RSD-ANN). The performance of RSD-ANN is far better than that of the existing methods. Based on our experiments, the average validation accuracy computed was 96.30% using either the tenfold cross-validation or holdout method.

MeSH terms

Heart Diseases* / diagnosis
Humans
Machine Learning*