Application of Explainable AI for Diagnosis of Coronary Heart Disease

Rishabh Jha; Amrita Singh; Dr. Anju Bhandari Gandhi1

1

Publication Date: 2025/01/02

Abstract: Coronary heart disease (CHD) is a leading global health challenge, necessitating early and accurate diagnostic methods to prevent adverse outcomes. This research explores the application of Explainable Artificial Intelligence (XAI) to enhance the diagnostic process. Leveraging CatBoost, a high-performing gradient boosting algorithm, this study achieves the maximum performance, minimizing false negatives and ensuring all potential CHD cases are identified. Furthermore, SHAP (SHapley Additive exPlanations) values are utilized to provide transparency in the model's decision-making process, addressing the opacity often associated with machine learning systems. The combination of high predictive performance and explainability demonstrates the feasibility of deploying AI systems in clinical decision-making for CHD.

Keywords: No Keywords Available

DOI: https://doi.org/ 10.5281/zenodo.14575931

PDF: https://ijirst.demo4.arinfotech.co/assets/upload/files/IJISRT24DEC988.pdf

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