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

Abdulaziz Albahr | Zendy; Marwan Ali Albahar | Zendy; Mohammed Thanoon | Zendy; Muhammad Binsawad | Zendy

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

Author(s) -

Abdulaziz Albahr,

Marwan Ali Albahar,

Mohammed Thanoon,

Muhammad Binsawad

Publication year - 2021

Publication title -

computational intelligence and neuroscience

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.605

H-Index - 52

eISSN - 1687-5273

pISSN - 1687-5265

DOI - 10.1155/2021/8628335

Subject(s) - computer science , regularization (linguistics) , heart disease , artificial intelligence , machine learning , predictive modelling , computational model , standard deviation , medicine , cardiology , mathematics , statistics

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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