TY - JOUR AU - Nazrul Anuar, Nayan AU - Hafifah, Ab Hamid AU - Mohd Zubir, Suboh AU - Noraidatulakma, Abdullah AU - Rosmina, Jaafar AU - Nurul Ain, Mhd Yusof AU - Mariatul Akma, Hamid AU - Nur Farawahida, Zubiri AU - Azwa Shawani, Kamalul Arifin AU - Syakila, Mohd Abd Daud AU - Mohd Arman, Kamaruddin AU - Rahman, A. Jamal PY - 2020/06/19 Y2 - 2024/03/29 TI - Cardiovascular Disease Prediction from Electrocardiogram by Using Machine Learning JF - International Journal of Online and Biomedical Engineering (iJOE) JA - Int. J. Onl. Eng. VL - 16 IS - 07 SE - Papers DO - 10.3991/ijoe.v16i07.13569 UR - https://online-journals.org/index.php/i-joe/article/view/13569 SP - pp. 34-48 AB - <p>Cardiovascular disease (CVD) is the leading cause of deaths worldwide. In 2017, CVD contributed to 13,503 deaths in Malaysia. The current approaches for CVD prediction are usually invasive and costly. Machine learning (ML) techniques allow an accurate prediction by utilizing the complex interactions among relevant risk factors. This study presents a case–control study involving 60 participants from The Malaysian Cohort, which is a prospective population-based project. Five parameters, namely, the R–R interval and root mean square of successive differences extracted from electrocardiogram (ECG), systolic and diastolic blood pressures, and total cholesterol level, were statistically significant in predicting CVD. Six ML algorithms, namely, linear discriminant analysis, linear and quadratic support vector machines, decision tree, k-nearest neighbor, and artificial neural network (ANN), were evaluated to determine the most accurate classifier in predicting CVD risk. ANN, which achieved 90% specificity, 90% sensitivity, and 90% accuracy, demonstrated the highest prediction performance among the six algorithms. In summary, by utilizing ML techniques, ECG data can serve as a good parameter for CVD prediction among the Malaysian multiethnic population.</p> ER -