心源性猝死
背景(考古学)
医学
朴素贝叶斯分类器
特征选择
人口
特征(语言学)
疾病
内科学
心脏病学
机器学习
人工智能
计算机科学
古生物学
语言学
哲学
环境卫生
支持向量机
生物
作者
Carlos Henrique Leitão Cavalcante,Pedro E. O. Primo,Carlos A. F. Sales,Weslley L. Caldas,João H. M. Silva,Amauri H. Souza,Emmanuel Silva Marinho,Roberto Coury Pedrosa,João Alexandre Lôbo Marques,Hélcio Silva dos Santos,João Paulo do Vale Madeiro
出处
期刊:Mathematical Biosciences and Engineering
[American Institute of Mathematical Sciences]
日期:2023-01-01
卷期号:20 (5): 9159-9178
被引量:1
摘要
About 6.5 million people are infected with Chagas disease (CD) globally, and WHO estimates that $ > million people worldwide suffer from ChHD. Sudden cardiac death (SCD) represents one of the leading causes of death worldwide and affects approximately 65% of ChHD patients at a rate of 24 per 1000 patient-years, much greater than the SCD rate in the general population. Its occurrence in the specific context of ChHD needs to be better exploited. This paper provides the first evidence supporting the use of machine learning (ML) methods within non-invasive tests: patients' clinical data and cardiac restitution metrics (CRM) features extracted from ECG-Holter recordings as an adjunct in the SCD risk assessment in ChHD. The feature selection (FS) flows evaluated 5 different groups of attributes formed from patients' clinical and physiological data to identify relevant attributes among 57 features reported by 315 patients at HUCFF-UFRJ. The FS flow with FS techniques (variance, ANOVA, and recursive feature elimination) and Naive Bayes (NB) model achieved the best classification performance with 90.63% recall (sensitivity) and 80.55% AUC. The initial feature set is reduced to a subset of 13 features (4 Classification; 1 Treatment; 1 CRM; and 7 Heart Tests). The proposed method represents an intelligent diagnostic support system that predicts the high risk of SCD in ChHD patients and highlights the clinical and CRM data that most strongly impact the final outcome.
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