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基于新型正则化器的机器学习预测心脏病的计算学习模型。

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

机构信息

College of Applied Medical Sciences, King Saud Bin Abdulaziz University for Health Sciences, Al-Ahsa 31982, Saudi Arabia.

Department of Science, Umm Al Qura University, P.O. Box 715, Mecca, Saudi Arabia.

出版信息

Comput Intell Neurosci. 2021 Nov 11;2021:8628335. doi: 10.1155/2021/8628335. eCollection 2021.

DOI:10.1155/2021/8628335
PMID:34804150
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8601816/
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.

摘要

心脏病是一种具有多种亚型的异质性疾病。早期诊断和预后对于患者的临床管理至关重要。在这项研究中，提出了一种用于预测早期心脏病的新计算模型。该预测模型嵌入在一种新的正则化方法中，该方法根据权重矩阵的标准差衰减权重，并将结果与其父代（RSD-ANN）进行比较。RSD-ANN 的性能明显优于现有方法。根据我们的实验，使用十折交叉验证或留一法计算的平均验证准确率分别为 96.30%和 96.09%。

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