CADDet: A Machine Learning Framework for Coronary Artery Disease Prediction Using Heart Sound Signals
Adabala Murali Veera Sri Sai1, Veeramreddy Umesh Reddy2, Pachigolla Anand Vijay Kumar Gupta3, Dakkili Likitha4, Vuda Srinivasarao5
1Adabala Murali Veera Sri Sai, Student, Department of Computer Science and Engineering, K L University, Vijayawada (Andhra Pradesh), India.
2Veeramreddy Umesh Reddy, Student, Department of Computer Science and Engineering, K L University, Vijayawada (Andhra Pradesh), India.
3Pachigolla Anand Vijay Kumar Gupta, Student, Department of Computer Science and Engineering, K L University, Vijayawada (Andhra Pradesh), India.
4Dakkili Likitha, Student, Department of Computer Science and Engineering, K L University, Vijayawada (Andhra Pradesh), India.
5Dr. Vuda Sreenivasarao, Associate Professor, Department of Computer Science and Engineering, K L University, Vijayawada (Andhra Pradesh), India.
Manuscript received on 31 October 2025 | First Revised Manuscript received on 09 November 2025 | Second Revised Manuscript received on 05 March 2026 | Manuscript Accepted on 15 March 2026 | Manuscript published on 30 March 2026 | PP: 1-7 | Volume-6 Issue-3 March 2026 | Retrieval Number: 100.1/ijpmh.A113206011125 | DOI: 10.54105/ijpmh.A1132.06030326
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© The Authors. Published by Lattice Science Publication (LSP). This is an open-access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Coronary artery disease (CAD) continues at the forefront of mortality sources across the globe. Its early detection using heart sound signals seems promising for integration into Wearable Body Area Networks (WBANs). On the other hand, WBAN-based CAD detection systems face challenges such as noise, motion artefacts, and poor signal quality, which in turn reduce diagnostic performance. Literature surveys indicate that most current models struggle due to insufficient feature extraction, fragile classification, and poor generalisation, leading to the outlined dilemma. We propose a robust classification algorithm that combines MFCC feature extraction with Random Forests to achieve high detection accuracy, addressing these problems and filling the research gap. For our research, we used the Heartbeat Sounds datasets from Kaggle, which encompass recordings from both clinical and non-clinical environments (Sets A and B). We derived 13 MFCC features per recording and employed an 80-20 stratified train-test split to balance the evaluation. The Random Forest classifier, powered by 100 decision trees, has achieved astonishing effectiveness, with 95% overall accuracy, 0.97 F1 Score for healthy cases, and 0.86 F1 Score for pathological cases. Our results exceed those of five recent baseline papers by a wide margin in precision, recall, and overall classification accuracy. Thus, they support the validity of the method we proposed for CAD detection using real heart sound data.
Keywords: Coronary Artery Disease, WBAN, Heart Sounds, MFCC, Random Forest, Classification.
Scope of the Article: Health Care Management