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Updated: Oct 12, 2025

In Silico Clinical Trials for Cardiovascular Disease
Published on: May 27, 2022
Computational Learning Model for Prediction of Heart Disease Using Machine Learning Based on a New Regularizer
Abdulaziz Albahr1, Marwan Albahar2, Mohammed Thanoon2
1College of Applied Medical Sciences, King Saud Bin Abdulaziz University for Health Sciences, Al-Ahsa 31982, 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.
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