Explainable AI for Heart Disease prediction: A Clinical Transparency Route Experiment

Title

Explainable AI for Heart Disease prediction: A Clinical Transparency Route Experiment

Creator

Kumar, Manoj; Sharan, Mudita; Shoran, Preety

Description

In this paper, a proposeable explainable machine learning procedure on estimating the danger of heart attack will be proposed with a stacked ensemble of XGBoost, Random Forest, and Multi-layered perceptron (MLP). The data set of UCI Heart Disease was preprocessed by normalization, imputation, and SMOTE to address the imbalance problem and the variables were optimized with the help of the feature engineering. The model performance was measured using accuracy, precision, recall, F1-score and ROC-AUC. In order to make the results more interpretable, Explainable AI were applied with SHAP and LIME, and the most relevant risk factors including troponin, cholesterol, and blood pressure were indicated.. In this paper, it is shown that ensemble learning in XAI can yield plausible, interpretable, and clinically practical data to complement enhanced cardiovascular diagnostics. 2025 IEEE.

Source

Proceedings of International Conference on Digital Innovations for Sustainable Solutions, ICDISS 2025;

Date

01-01-2025

Publisher

Institute of Electrical and Electronics Engineers Inc.

Subject

Clinical Decision Support; Ensemble Learning; Ex explainable AI; Heart Attack Prediction; Random Forest; SHAP; XGBoost

Coverage

Kumar M., Department of Computer Sciences, CHRIST University, Bangalore, India; Sharan M., Department of Computer Sciences, CHRIST University, Bangalore, India; Shoran P., Department of Computer Sciences, CHRIST University, Bangalore, India

Rights

Restricted Access; Hardcopy may be available in the library

Relation

ISBN: 979-833155641-9;

Format

online

Language

English

Type

Conference paper

Identifier

https://doi.org/10.1109/ICDISS68238.2025.11320656

https://www.scopus.com/pages/publications/105033597851?origin=resultslist