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

被引：6
|
作者：
Albahr, Abdulaziz [1 ]
Albahar, Marwan [2 ]
Thanoon, Mohammed [2 ]
Binsawad, Muhammad [3 ]
机构：
[1] King Saud Bin Abdulaziz Univ Hlth Sci, Coll Appl Med Sci, Al Hasa 31982, Saudi Arabia
[2] Umm Al Qura Univ, Dept Sci, POB 715, Mecca, Saudi Arabia
[3] King Abdulaziz Univ, Comp Informat Syst Dept, Jeddah, Saudi Arabia
来源：
COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE | 2021年 / 2021卷
关键词：
FEATURE-SELECTION; DIMENSION REDUCTION; GENETIC ALGORITHM;
D O I：
10.1155/2021/8628335
中图分类号：
Q [生物科学];
学科分类号：
07 ; 0710 ; 09 ;
摘要：
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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收藏
页数：10
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