An Explainable AI-Driven Deep Learning Algorithm for Heart Disease Detection in Healthcare

Title

An Explainable AI-Driven Deep Learning Algorithm for Heart Disease Detection in Healthcare

Creator

Singh, Aaron; Alapatt, Bosco Paul; George, Jossy P

Description

The application of preprocessed Kaggle data serves as a subject of analysis to investigate heart attack prediction capabilities through machine learning models. The research examines performance outcomes of five algorithms which consist of K-Nearest Neighbors (KNN), Random Forest, Support Vector Machine (SVM), Extreme Gradient Boosting (XGBoost) and Convolutional Neural Networks (CNN). Random Forest together with XGBoost proved as the most accurate machine learning models when used for cardiovascular risk assessment. The researchers built a hybrid structure of CNN and SVM because it improved both data classification and feature extraction processes for better prediction outcomes. The training and evaluation process of models encountered difficulties because of overfitting along with high computational expenses and problems regarding optimal hyperparameter settings. The research stresses that explainable AI (XAI) methods should integrate into systems to enhance model interpretability and achieve trust from clinical professionals. Future initiatives seek real-time patient monitoring and innovative interpretability systems for heart attack prediction to enable person-specific diagnoses and optimal clinical choices in medical fields. 2025 IEEE.

Source

Proceedings of 6th International Conference on Intelligent Communication Technologies and Virtual Mobile Networks, ICICV 2025;pp.471-477

Date

01-01-2025

Publisher

Institute of Electrical and Electronics Engineers Inc.

Subject

Cardiovascular Disease; CNN-SVM Hybrid; Healthcare AI; Heart Attack Prediction; Machine Learning; Medical Diagnostics; Predictive Analytics; Random Forest; XGBoost

Coverage

Singh A., Christ University, India; Alapatt B.P., Christ University, India; George J.P., Christ University, India

Rights

Restricted Access; Hardcopy may be available in the library

Relation

ISBN: 979-833151175-3;

Format

online

Language

English

Type

Conference paper

Identifier

https://doi.org/10.1109/ICICV64824.2025.11085482

https://www.scopus.com/pages/publications/105013073834?origin=resultslist