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All Journal International Journal of Advances in Applied Sciences Jurnal Ilmiah Rekayasa dan Manajemen Sistem Informasi Jurnal Komputer Terapan Dinamisia: Jurnal Pengabdian Kepada Masyarakat JITER-PM (Jurnal Inovasi Terapan - Pengabdian Masyarakat)
Muhammad mahrus zain
Politeknik Caltex Riau
Computer Science & IT Earth & Planetary Sciences Economics, Econometrics & Finance Education Engineering Environmental Science Health Professions Materials Science & Nanotechnology Mathematics Physics Social Sciences Other

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Journal : International Journal of Advances in Applied Sciences

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Electrocardiogram signals classification using random forest method for web-based smart healthcare Juni Nurma Sari; Putri Madona; Hari Kusryanto; Muhammad Mahrus Zain; May Valzon
International Journal of Advances in Applied Sciences Vol 12, No 2: June 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijaas.v12.i2.pp133-143

Abstract

Coronary heart is the highest cause of death in Indonesia reaching 26%. Therefore, to prevent the high mortality rate of coronary heart disease (CHD), early detection of CHD can be carried out. One way is to examine the electrocardiogram/electrocardiograph (ECG) recording. ECG hardware has been made in previous studies to record ECG signals. ECG research is an important study because it can detect cardiovascular disease. Cardiovascular diseases can be classified as arrhythmic diseases. Arrhythmia is a disorder that occurs in the heart rhythm. The method used to recognize and classify ECG signal patterns is the R-R interval (RRI) method. In this study, the ECG signal is classified as normal and abnormal. Abnormal means that a person has a heart rhythm disorder. The classification method used is random forest. The advantage of the random forest classifier is that it can handle noise and missing values and can handle large amounts of data. The accuracy of the ECG signal classification using the Random forest method is 96%. The contribution of this research is that early detection of heart rhythm disorders using an ECG can be monitored through the smart healthcare web.
Co-Authors Ardianto Wibowo Erzi Hidayat Hanna Junita B. Tampubolon Hari Kusryanto Heni Rachmawati Indah Lestari Indah Vusvita Sari Juni Nurma Sari Madona, Putri Meilany Dewi mutia sari zulvi Satria Perdana Arifin Valzon, May Yohana Dewi Lulu Widyasari