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

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 pre...

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Detalles Bibliográficos
Autores principales: Albahr, Abdulaziz, Albahar, Marwan, Thanoon, Mohammed, Binsawad, Muhammad
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2021
Materias:
Research Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8601816/
https://www.ncbi.nlm.nih.gov/pubmed/34804150
http://dx.doi.org/10.1155/2021/8628335
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author Albahr, Abdulaziz
Albahar, Marwan
Thanoon, Mohammed
Binsawad, Muhammad
author_facet Albahr, Abdulaziz
Albahar, Marwan
Thanoon, Mohammed
Binsawad, Muhammad
author_sort Albahr, Abdulaziz
collection PubMed
description 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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spelling pubmed-86018162021-11-19 Computational Learning Model for Prediction of Heart Disease Using Machine Learning Based on a New Regularizer Albahr, Abdulaziz Albahar, Marwan Thanoon, Mohammed Binsawad, Muhammad Comput Intell Neurosci Research Article 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. Hindawi 2021-11-11 /pmc/articles/PMC8601816/ /pubmed/34804150 http://dx.doi.org/10.1155/2021/8628335 Text en Copyright © 2021 Abdulaziz Albahr et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Albahr, Abdulaziz
Albahar, Marwan
Thanoon, Mohammed
Binsawad, Muhammad
Computational Learning Model for Prediction of Heart Disease Using Machine Learning Based on a New Regularizer
title Computational Learning Model for Prediction of Heart Disease Using Machine Learning Based on a New Regularizer
title_full Computational Learning Model for Prediction of Heart Disease Using Machine Learning Based on a New Regularizer
title_fullStr Computational Learning Model for Prediction of Heart Disease Using Machine Learning Based on a New Regularizer
title_full_unstemmed Computational Learning Model for Prediction of Heart Disease Using Machine Learning Based on a New Regularizer
title_short Computational Learning Model for Prediction of Heart Disease Using Machine Learning Based on a New Regularizer
title_sort computational learning model for prediction of heart disease using machine learning based on a new regularizer
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8601816/
https://www.ncbi.nlm.nih.gov/pubmed/34804150
http://dx.doi.org/10.1155/2021/8628335
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