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

Journal: Computational intelligence and neuroscience
Published Date: Nov 11, 2021

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.

Authors

  • Abdulaziz Albahr
    College of Applied Medical Sciences, King Saud Bin Abdulaziz University for Health Sciences, Al-Ahsa 31982, Saudi Arabia.
  • Marwan Albahar
    Department of Science, Umm Al Qura University, P.O. Box 715, Mecca, Saudi Arabia.
  • Mohammed Thanoon
    Department of Science, Umm Al Qura University, P.O. Box 715, Mecca, Saudi Arabia.
  • Muhammad Binsawad
    King Abdulaziz University, Computer Information System Department, Jeddah, Saudi Arabia.

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