Cardiac abnormalities detection using deep learning techniques

Cardiovascular diseases present a substantial health concern globally, affecting individuals across diverse populations, including members of the Malaysian Armed Forces. The early identification of cardiac irregularities is crucial for timely intervention and reducing associated health risks. This research endeavors to assess and compare the efficacy of various supervised machine learning techniques in accurately detecting cardiac abnormalities, recognizing the unique lifestyle and health dynamics inherent to military personnel. A comprehensive dataset comprising numerical electrocardiograms (ECGs) and three years' worth of clinical data from Malaysian Armed Forces personnel was meticulously collected for analysis. Rigorous data preprocessing, encompassing cleaning and normalization procedures, was conducted to ensure the robustness of the models employed. Evaluation of model performance was carried out utilizing metrics such as accuracy and precision, discerned through confusion matrices, affirming the model's capability to differentiate between normal and abnormal cardiac conditions effectively. The outcomes underscore the potency of deep learning methodologies in precisely discerning a spectrum of cardiac abnormalities, spanning from arrhythmias to hypertrophy and structural anomalies. This research marks a notable stride forward in the realm of cardiac abnormality detection within the Malaysian Armed Forces, laying the groundwork for potential integration into routine healthcare protocols.