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A Novel Approach to Dual Feature Selection of Atrial Fibrillation Based on HC-MFS
Hong Liu1,2, Lifeng Lu1, Honglin Xiong3,4
1Business School, University of Shanghai for Science and Technology, Shanghai 200093, China.
Insights
This study identified key risk factors for atrial fibrillation (AF) in Shanghai, finding seasonal variations and inflammatory markers like C-reactive protein are significant predictors. Machine learning models effectively diagnose individuals predisposed to AF.
Area of Science:
- Cardiology
- Medical Informatics
- Public Health
Background:
- Atrial fibrillation (AF) is a prevalent arrhythmia with complex risk factors.
- Identifying specific risk factors in diverse populations is crucial for targeted prevention and management.
- Previous studies have explored various indicators, but a comprehensive approach integrating clinical and environmental factors is needed.
Purpose of the Study:
- To identify and analyze risk factors for atrial fibrillation (AF) in Shanghai's Chongming District.
- To develop and evaluate a novel dual feature-selection methodology (HC-MFS) for improved AF risk prediction.
- To assess the utility of machine learning models in diagnosing AF predisposition.
Main Methods:
- Data from 678 patients treated for AF between 2020-2023 in Chongming District, Shanghai, were analyzed.
- A novel hierarchical clustering with Fisher scores (HC-MFS) feature selection method was developed and benchmarked.
- Five classification models were trained and evaluated using the HC-MFS approach, with performance validated on a test set.
Main Results:
- The HC-MFS method achieved superior performance, with the highest accuracy (0.9118) and lowest root mean square error (0.2970) in AF classification.
- Seasonal variations were identified as a primary risk factor for AF (correlation ranks: pr=0.31, FS=0.11, DCFS=0.33).
- Inflammatory markers (C-reactive protein) and lipid profiles (LDL, total cholesterol) along with platelet count were significant indirect risk factors for AF.
Conclusions:
- The novel HC-MFS feature selection method significantly enhances the accuracy of AF risk prediction models.
- Machine learning models, informed by pathological and climatic factors, can effectively aid clinicians in diagnosing AF predisposition.
- Seasonal variations and inflammatory markers are critical, often overlooked, risk factors for AF in the studied population.
Abstract:
This investigation sought to discern the risk factors for atrial fibrillation within Shanghai's Chongming District, analyzing data from 678 patients treated at a tertiary hospital in Chongming District, Shanghai, from 2020 to 2023, collecting information on season, C-reactive protein, hypertension, platelets, and other relevant indicators. The researchers introduced a novel dual feature-selection methodology, combining hierarchical clustering with Fisher scores (HC-MFS), to benchmark against four established methods. Through the training of five classification models on a designated dataset, the most effective model was chosen for method performance evaluation, with validation confirmed by test set scores. Impressively, the HC-MFS approach achieved the highest accuracy and the lowest root mean square error in the classification model, at 0.9118 and 0.2970, respectively. This provides a higher performance compared to existing methods, thanks to the combination and interaction of the two methods, which improves the quality of the feature subset. The research identified seasonal changes that were strongly associated with atrial fibrillation (pr = 0.31, FS = 0.11, and DCFS = 0.33, ranked first in terms of correlation); LDL cholesterol, total cholesterol, C-reactive protein, and platelet count, which are associated with inflammatory response and coronary heart disease, also indirectly contribute to atrial fibrillation and are risk factors for AF. Conclusively, this study advocates that machine-learning models can significantly aid clinicians in diagnosing individuals predisposed to atrial fibrillation, which shows a strong correlation with both pathological and climatic elements, especially seasonal variations, in the Chongming District.
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