A Hybrid Machine Learning and Deep Learning Framework for ECG-Based Cardiac Abnormality Detection

Abstract

Cardiovascular diseases remain a leading cause of global mortality, underscoring the need for reliable and efficient diagnostic tools. Thisthesis investigates machine learn ing and deep learning approaches for detecting cardiac abnormalities using electro cardiogram (ECG) data. Initial experiments explored classical heart disease datasets; however, due to their limited scale and clinical relevance, the research pivoted to the PTB-XL+dataset,whichprovidestabularECGfeaturesextractedfrom12-leadrecord ings. Acomprehensive set of baseline models was implemented, includingtraditional clas sifiers (Logistic Regression, Support Vector Machine, Random Forest, Naïve Bayes, XGBoost, LightGBM, CatBoost) and deep learning architectures (Shallow MLP, MLP, TabNet, ResMLP,DANet,AutoInt). ModelswereevaluatedonthepredefinedPTB-XL folds using accuracy, precision, recall, F1-score, ROC-AUC, and confusion matrices. The best-performing baseline models were gradient boosting ensembles (XGBoost, LightGBM,CatBoost)andselectedneuralnetworks(MLP,DANet,ResMLP).Building upon these results, a stacked meta-learning framework was employed, with Logistic Regression, LightGBM, and Shallow MLP as meta learners. Two strategies were ex plored: (i) usingmetafeaturesonlyand(ii)combiningmetafeatureswithoriginalfea tures. The hybrid approach achieved the highest performance, with LightGBM_Full attaining an accuracy of87.7%,F1-scoreof0.876, andROC-AUCof0.947,outperform ing individual baselines. These findings demonstrate the effectiveness of stacking ensembles for ECG-based classification, particularly when integrating meta features with original features. The research contributes a systematic benchmarking of models on PTB-XL+, highlights the superiority of boosting-based ensembles for tabular ECG features, and establishes a reproducible framework that can guide future clinical decision support systems.