PO-03-207 BUILDING A PREDICTION MODEL FOR SUDDEN CARDIAC DEATH IN A GENERAL POPULATION IN TAIWAN USING THE MACHINE LEARNING TECHNIQUE
نویسندگان
چکیده
Predicting the risk of sudden cardiac death (SCD) is paramount importance in preventive medicine. However, it remains a major challenge to prevent SCD general population. This study aimed develop prediction model for deaths population using community-based cohort Taiwan. Chin-Shan Community Cardiovascular Cohort (CCCC) enrolled participants ≥ 35 years age since 1990-1991, and were followed up until 2005. Participants without 12-lead ECG, echocardiography, carotid artery duplex sonography data excluded from this study. A total 2193 subjects CCCC analyzed (Cohort 1: whole CCCC). Among 1, 2105 prior history CAD heart failure (HF) with reduced ejection fraction (HFrEF: left ventricular [LVEF] < 35%) also studied 2). For 1 & 2, we randomly selected 55% be training dataset, 30% as validation 15% test dataset. Feature machine learning (ML) was applied assign score input features based on how important they are predicting events. Four ML algorithms compared, including: (1) XGboost; (2) random forests; (3) logistic regression; (4) DNN (deep neural network). The receiver operating characteristic curves area under (AUC) used summarize performance. cumulative incidence 1.50% 1. AUC CCCC-SCD-Score (previously published by our research team) risks 0.888. Using analysis, established novel models predict within range 0.81-1.00 (using XGboost forests (Figure). scores feature variable factors 2 shown Figure. coronary disease (CAD) diagnosed electrocardiography (ECG) or most significant determinant among all factors; CCCC-SCD team factors. excellent events via forests. Careful evaluation management may useful
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ژورنال
عنوان ژورنال: Heart Rhythm
سال: 2023
ISSN: ['1556-3871', '1547-5271']
DOI: https://doi.org/10.1016/j.hrthm.2023.03.944