Problem
Research questions and friction points this paper is trying to address.
Chronic Kidney Disease
Early Detection
Psychosocial Factors
Risk Stratification
Population Health-Based Machine Learning Reveals Associations Between Psychosocial Factors and Chronic Kidney Disease
🤖 AI Summary
研究使用大规模远程健康数据与机器学习方法，识别慢性肾病的关键驱动因素，通过处理缺失数据和不平衡类别问题，实现疾病分类并提高早期检测能力。
📝 Abstract
Chronic kidney disease (CKD) progresses silently and severely undermines quality of life, making early detection critical for improving patient outcomes. We present a two-part study that combines large-scale telehealth data with advanced machine learning to both classify self-reported CKD status and identify key drivers of disease. Using selected features from the Behavioral Risk Factor Surveillance System (BRFSS 2021: 438,693 samples; BRFSS 2019: 418,268 samples) and the National Health Interview Survey (NHIS 2021: 29,482 samples; NHIS 2020: 31,568 samples), we addressed missing data with nine state-of-the-art imputation methods and mitigated class imbalance via sampling strategies. Our customized stacked ensemble model achieved balanced accuracy of 72.56-76.12%, with corresponding AUROC scores of 79.59-82.29%. SHapley Additive exPlanations (SHAP) analysis, followed by clinical review, highlighted critical predictors, including regular medical check-ups, age, blood pressure, and indicators of mental health stress. These findings deliver a robust and interpretable framework for CKD risk stratification and provide actionable insights into its associated factors.
Innovation
Methods, ideas, or system contributions that make the work stand out.
machine learning
chronic kidney disease
data imputation
ensemble model
SHAP analysis
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