Computational Learning Model for Prediction of Heart Disease Using Machine Learning Based on a New Regularizer. (11th November 2021)

• Record Type: Journal Article
• Authors: Albahr, Abdulaziz; Albahar, Marwan; Thanoon, Mohammed; Binsawad, Muhammad
• Other Names: Versaci Mario Academic Editor.
• Is Part Of: Computational intelligence and neuroscience. Volume 2021(2021)
• Publication Date: 2021-11-11
• Subjects: Neurosciences -- Data processing -- Periodicals; Computational intelligence -- Periodicals; Computational neuroscience -- Periodicals; 612.80285
• DOI: 10.1155/2021/8628335
• Languages: English
• ISSNs: 1687-5265

Abstract

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.