Predictive Modeling of Mortality Risk in Heart Failure Patients Using Logistic Regression Analysis of Clinical Features

Abstract

Heart failure represents a significant global health challenge with substantial mortality rates. This study develops and validates a logistic regression-based predictive model for mortality risk assessment in heart failure patients using comprehensive clinical records. Analyzing data from 299 patients across 12 clinical features, we achieved a model performance with an area under the receiver operating characteristic curve (AUC-ROC) of 0.872 and accuracy of 80.0%. Our analysis identified follow-up time (coefficient: -1.622, p<0.001), ejection fraction (coefficient: -1.159, p<0.001), age (coefficient: +0.541, p=0.018), and serum creatinine (coefficient: +0.429, p=0.062) as the most influential predictors of mortality. The model demonstrates robust calibration characteristics and provides clinically actionable risk stratification, suggesting its potential utility in supporting clinical decision-making for heart failure patient management and personalized treatment planning.