Machine learning model identifies key risk factors for chronic kidney disease

Researchers have developed a machine learning framework to identify individuals at risk for chronic kidney disease (CKD). By analyzing data from large-scale telehealth surveys like the Behavioral Risk Factor Surveillance System and the National Health Interview Survey, their model achieved a balanced accuracy of up to 76.12% and an AUROC score of 82.29%. Further analysis using SHapley Additive exPlanations pinpointed key risk factors, including regular medical check-ups, age, blood pressure, and mental health stress indicators, offering a pathway for improved CKD risk stratification. AI

IMPACT Provides a framework for early detection and risk stratification of chronic kidney disease, potentially improving patient outcomes.

RANK_REASON The cluster contains a research paper detailing a new machine learning model and its application to health data. [lever_c_demoted from research: ic=1 ai=1.0]

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• Behavioral Risk Factor Surveillance System
• BRFSS 2019
• BRFSS 2021
• chronic renal insufficiency
• National Health Interview Survey
• NHIS 2020
• NHIS 2021
• SHAP
• Shapley Additive Explanations
• Tanmoy Sarkar Pias

• paper
• model release
• other

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