A Dual-Framework Approach for Improved Heart Disease Detection: CGAN-Based Data Augmentation and DAE Driven Feature Extraction
Corresponding author: Saeb Wfi Hanash
Abstract
This study proposes two different frameworks that incorporate Deep Learning (DL) and Machine Learning (ML) for enhancing heart disease detection across Cleveland, Erbil, and Comprehensive datasets. The first framework integrated Conditional Generative Adversarial Networks (CGANs) with a Random Forest (RF) classifier optimized using the Categorical and Continuous Covariance Matrix Adaptation (CatCMA) algorithm, while the second one utilized a Denoising Autoencoder (DAE) with a Voting Ensemble composed of three classifiers: Support Vector Machine (SVM), Multilayer Perceptron (MLP), and RF. The results showed that both frameworks achieved a higher level of performance compared to traditional methods, with an accuracy of up to 97.01% and 100% recall. Overall, the integration of data augmentation and feature enhancement with optimized classification models seems to be a potential technique for heart disease prediction. Future studies should explore the real-time implementation of various data integration methods to enhance the connection between research and clinical practice.
Keywords:
heart disease prediction, machine learning, deep learning, CGAN, denoising autoencoderDownloads
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Copyright (c) 2025 Saeb Wfi Hanash, Ahmad Saeed Mohammad, Sarmad K. Ibrahim
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