Predicting Cognitive Decline In Heart Failure Patients Using Ml Based Multi Parameter Risk Scoring

Patients with heart failure (HF) may experience cognitive decline, a commonly disregarded but clinically significant complication that lowers quality of life, increases hospitalization, and impairs self-care. This study suggests a multi-parameter risk scoring framework based on machine learning (ML) to anticipate cognitive decline in HF patients early on. The study employed a dataset that included clinical, biochemical, imaging, and neuropsychological parameters. These variables included baseline cognitive scores (e.g., MMSE, MoCA), brain MRI findings, NT-proBNP levels, and left ventricular ejection fraction (LVEF). After training and evaluating a number of supervised learning models, including Random Forest, XGBoost, and Support Vector Machines, XGBoost performed the best (AUC = 0.91, sensitivity = 87.3%, specificity = 85.6%). Hippocampal volume, LVEF, and NT-proBNP were identified as important predictors by feature importance analysis. Strong predictive ability was demonstrated when the resulting risk score was validated against longitudinal cognitive decline over a 12-month period. The results demonstrate the clinical value of incorporating machine learning (ML) tools into cardiovascular care for prompt intervention and proactive cognitive monitoring.