Cardiovascular Disease Prediction from Electrocardiogram by Using Machine Learning
| Autor: | Suboh Mohd Zubir, Abdullah Noraidatulakma, A. Jamal Rahman, Zubiri Nur Farawahida, Mhd Yusof Nurul Ain, Kamaruddin Mohd Arman, Jaafar Rosmina, Mohd Abd Daud Syakila, Kamalul Arifin Azwa Shawani, Nayan Nazrul Anuar, Ab Hamid Hafifah, Hamid Mariatul Akma |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
education.field_of_study
Artificial neural network Cvd risk business.industry ecg Computer applications to medicine. Medical informatics Population rmssd R858-859.7 General Engineering Decision tree Disease Machine learning computer.software_genre Linear discriminant analysis cvd Support vector machine machine learning the malaysian cohort Cohort Medicine Artificial intelligence business education computer |
| Zdroj: | International Journal of Online and Biomedical Engineering, Vol 16, Iss 07, Pp 34-48 (2020) |
| ISSN: | 2626-8493 |
| DOI: | 10.3991/ijoe.v16i07.13569 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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