Leveraging Explainable AI for Improved Heart Failure Survival Prediction Models - Tiwari, Mukesh Kumar; Singh, Brij Mohan (Journal of Neonatal Surgery, 2025)
Heart failure (HF) is a major cause of illness and death globally, highlighting the need for effective prediction models to identify high-risk patients. Traditional machine learning models are worked as a “black boxes”, offering little understanding of how they make decisions. This study 1) Present a comparative analysis of conventional machine learning models on the HF disease. 2) examines how to incorporate eXplainable Artificial Intelligence (XAI) techniques into heart failure conventional survival prediction models. 3) Analyzes the explainability of heart failure (HF) survival prediction models. This study analyzes a dataset containing 918 patient records with a history of HF. In the first phase of the study, the machine learning model Xtreme Gradient Boosting (XGB) achieves the highest accuracy of 88.59% among all models tested on the HF dataset. The second phase focuses on explainability, emphasizing that cholesterol levels, age, MaxHR, and Oldpeak are crucial features in HF prediction. With the analysis of the experts note that the model performs well because these relevant features significantly contribute to predicting HF and can save the human life.