A Hybrid Stacked Ensemble Model for Heart Disease Prediction

Title

A Hybrid Stacked Ensemble Model for Heart Disease Prediction

Creator

Baruah, Anamika; Poonia, Ramesh Chandra; Shanbhog, Manjula

Description

Cardiovascular Diseases (CVDs), especially heart attacks, are resulting in high rates of death worldwide, which highlights the need for early prediction systems. This paper deals with advanced ML and DL methods to predict heart attacks with a pre-processed clinical dataset. 6 models were used: a Hybrid Stacked Model combined with Logistic Regression, Random Forest, and XGBoost using a neural meta-learner; CNN with LSTM, BiGRU, and dense layers; an RNN with BiLSTM; and an XGBoost method using deep feature representations. Data preprocessing involved feature scaling and class balancing with the help of SMOTE. Model performance is being measured by Accuracy, Precision, Recall, and F1-Score. Hybrid Stacked Model had the highest accuracy (94.24%) and F1-score (94.12%), while CNN + LSTM had the best recall (95.96%), to reduce false negatives. XGBoost with deep features demonstrated competitive accuracy (91.22%) and transparency. These results point to the efficiency of hybrid and sequential deep learning models in cardiovascular risk prediction. In the future, research will be focused on real-time patient data integration, federated learning for privacy, and personalized health promotion using IoT-based monitoring. 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

BiGRU; Cardiovascular Diseases; CNN; Heart Attack Predication; LSTM

Coverage

Baruah A., Department of Computer Science, CHRIST University, Bangalore, India; Poonia R.C., Department of Computer Science, CHRIST University, Bangalore, India; Shanbhog M., Department of Computer Science, 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/ICDISS68238.2025.11320645

https://www.scopus.com/pages/publications/105033633612?origin=resultslist