Semi-Parametric and Accelerated Failure Time Survival Models for Heart Failure Prediction

Abstract

Heart failure is a leading cause of death among Diabetic and Obese patients globally, contributing to 8.5% of all heart disease deaths and potentially 36% of cardiovascular disease deaths. Early detection is crucial for timely intervention, reducing symptoms, lowering hospitalizations, and improving patient outcomes through personalized management. This paper presents the use of Semi-Parametric and Accelerated Failure Time survival models (AFT) on Heart failure prediction. Cox Proportional Hazard model from the family of Semi-Parametric survival models and Weibull's Accelerated Failure Time models from the family of Accelerated Failure Time models have been compared and contrasted. The Cox PH model excels in its ability to adapt to various survival time distributions avoiding any assumptions on survival time distribution. The Cox proportional hazard allows for the examination of covariate effects on the hazard function, making it a widely used survival model. Weibull AFT model follows a parametric approach, directly estimating the distribution of the survival time. Medical records data of close to 299 patients, who had heart failure, collected during their clinical followup period is used for building and evaluating the model. The final evaluation of model performance was conducted, focusing on their capacity to predict the probability of patient survival beyond 250 days from the clinical visit. Among the Cox proportional hazard model and Weibull's AFT model, the Weibull's AFT model demonstrated superior performance compared to the Cox model. Remarkably, Weibull's model exhibited consistently exceptional performance in both train and test validations.