An Explainable Federated Learning Approach for Heart Disease Classification
| dc.contributor.author | Sathi, Tanjila Alam | |
| dc.date.accessioned | 2026-07-28T10:00:26Z | |
| dc.date.issued | 2025-10-25 | |
| dc.description | Supervised by Dr. Md. Azam Hossain, Associate Professor, Department of Computer Science and Engineering (CSE) Islamic University of Technology (IUT) Board Bazar, Gazipur, Bangladesh. This thesis is submitted in partial fulfillment of the requirement for the degree of Master of Science in Computer Science and Engineering, 2025 | |
| dc.description.abstract | Cardiovascular disease (CVD) is a life-threatening medical condition that affects the heart and blood vessels, leading to substantial financial and social burdens. Electro cardiograms (ECG) are extensively used to detect and treat heart diseases as these are rapid, secure, non-invasive, and cost-effective. The ECG signal has several segments, including the P-wave, QRS complex, QT interval, ST segment, and T-wave, together with the associated onset, offset, and peak points referred to as fiducial points. Ab normalities in these components can be indicative of various heart diseases. Medical professionals face challenges when using the conventional approach to ECG record ing, including comprehending the intricate nature of the ECG to interpret, dealing with signal interference, and handling the frequent coexistence of other health conditions alongside heart disease. However, accurate identification of fiducial points associated with the ECG wave is crucial for proper assessment of heart disease. To automate the diagnosis of heart diseases by analyzing ECG signals, various machine learning and deep learning classification models are used where data must be centralized. The majority of contemporary research encounters challenges due to data privacy, limited dataset, and lack of collaboration and proper explanation of the result. To address these challenges, this thesis presents an explainable federated learning model for the classi fication of heart disease using ECG fiducial features. This approach is the first of its kind to classify three distinct heart conditions: arrhythmia, ischemia and healthy states while ensuring both data privacy, as well as the interpretability of the outcome. We in troduced two models for classifying three distinct heart conditions: Federated Learning with Artificial Neural Networks (FL-ANN) and Federated learning with long-short term memory (FL-LSTM), which achieved impressive accuracies of 87% and 90%, respectively. In addition, explainable artificial intelligence(XAI) models can improve trust by providing visual interpretations of their results and decisions. The proposed explainable models highlighted the fiducial features of the ECG, including P-wave height (P-H), R-wave height (R-H), the QRS complex, heart rate (HR), QT interval, and corrected QT interval (QTc), which provide invaluable insights into the heart’s electrical activity, serve as vital markers for cardiac disorders, and significantly im prove diagnostic precision. Our explainable federated learning models facilitate data collaboration while safeguarding the privacy of sensitive information. By ensuring the explainability of the outcome, these models are designed to support healthcare profes sionals, helping them make better and more accurate diagnostic decisions. | |
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| dc.identifier.uri | https://repository.iutoic-dhaka.edu/handle/123456789/2762 | |
| dc.language.iso | en | |
| dc.publisher | Department of Computer Science and Engineering (CSE) Islamic University of Technology (IUT) Board Bazar, Gazipur, Bangladesh | |
| dc.title | An Explainable Federated Learning Approach for Heart Disease Classification | |
| dc.type | Thesis |
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