International Journal of Science, Engineering and TechnologyAuthors: Riya Kapil, Riyanshu Saini, Ashish Srivastava
Abstract: The majority of deaths worldwide are caused by heart disease. Doctors can use machine learning (ML) models to predict the risk of heart disease from routine exams and tests. The use of black-box ML models in healthcare is hindered by their lack of explanation for prediction. The proposed EHD-ML system combines effective ML models, such as gradient boosted trees and neural net-works, with techniques for explain ability that can be ap-plied to any model. SHAP, LIME, rule extraction, and counterfactual explanations are just a few of the things that are included. We cover dataset preparation, feature engineering, model training, interpretability pipelines, evaluation metrics like accuracy, AUC, F1, and calibra-tion, along with user-friendly explanations for clinicians, such as feature importance and patient-level explana-tions. We also outline the software and hardware design for deployment and suggest validation through retrospec-tive studies and prospective clinical trials. The key contri-butions include: (1) an end-to-end pipeline focused on explain ability for heart disease prediction, (2) a compar-ative analysis of interpretability methods and how accu-rately they reflect model predictions, and (3) user-centered explanation templates tailored for clinical use..
DOI: http://doi.org/10.5281/zenodo.20339115
