RAVE-HD: A Novel Sequential Deep Learning Approach for Heart Disease Risk Prediction in e-Healthcare.

Area of Science:

• Cardiology
• Artificial Intelligence
• Machine Learning

Background:

• Heart disease (HD) is a leading global cause of mortality, necessitating improved diagnostic tools.
• Existing Internet of Things (IoT)-enabled machine learning for HD screening faces challenges like imbalanced data and feature selection.
• Early and accurate HD diagnosis is crucial for improving patient outcomes.

Purpose of the Study:

• To develop a robust and explainable machine learning approach for enhanced heart disease screening.
• To address limitations in existing HD screening methods, including data imbalance and feature identification.
• To present the RAVE-HD (ResNet And Vanilla RNN Ensemble for HD) approach for improved diagnostic accuracy.

Main Methods:

• A sequential hybrid approach integrating Residual Network (ResNet) and Vanilla Recurrent Neural Network (RNN).
• Preprocessing included duplicate removal, feature scaling, Recursive Feature Elimination, and synthetic data sampling for class imbalance.
• The RAVE model was trained and validated on the HDHI medical dataset, with cross-dataset validation on the CVD dataset.

Main Results:

• The RAVE model achieved 92.06% accuracy and 97.12% ROC-AUC, outperforming baseline models.
• Robustness was confirmed by 10-fold cross-validation, Sensitivity-to-Prevalence analysis, and bootstrap/DeLong tests (p<0.001).
• SHAP analysis provided model interpretability, and cross-dataset validation showed strong generalization (92.4% accuracy).

Conclusions:

• RAVE-HD demonstrates significant promise as a reliable, explainable, and scalable solution for large-scale heart disease screening.
• The approach offers clinically meaningful improvements and acts as a practical decision-support tool for predictive screening.
• RAVE-HD's consistent performance across diverse evaluations and datasets highlights its potential in clinical settings.