A Comparative Machine Learning Analysis for Early Atrial Fibrillation Prediction
Keywords:
Atrial fibrillation, Cardiovascular diseases, Early prediction, Machine learning methodsAbstract
Cardiovascular Diseases (CVD) are significant global cause of mortality. This paper focuses on early detection of a specific type of CVD, Atrial Fibrillation (AF), through a simple approach. The methodology is based on efficient risk assessment methods to identify high-risk individuals with a comparative analysis of seven ML algorithms to find the simplest and most effective approach. The research utilizes the Sleep Heart Health Study (SHHS) dataset, a large-scale cohort study with diverse clinical parameters and polysomnographic data, which seems to be ideal for early AF prediction. The study formulates a predictive analysis based on minimal accessible data (i.e. no signal, image, or complex measurement are considered) and evaluates seven ML algorithms including Support Vector Machines (SVM), K-Nearest Neighbor (KNN), Random Forest (RF), Decision Tree (DT), Gradient Boosting (GB), Multi-Layer Perceptron (MLP), and Logistic Regression (LR). Among these methods, LR shows notable predictive accuracy. The analysis covers a diverse cohort, including various races (i.e. White, Black, and others), ethnicities, and both genders, with a focus on individuals with aged averagely more than 63. The study concludes that our formulation with the simple and readily accessible parameters predict AF reasonably well, potentially enabling early interventions to reduce morbidity and mortality.
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Copyright (c) 2025 Badrosadat Nategholeslam Shirazi, Shiva Naghsh (Author); Ali Akbar Safavi; Amir Sharafkhaneh (Author)
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
