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All Journal International Journal of Power Electronics and Drive Systems (IJPEDS) Indonesian Journal of Electronics and Instrumentation Systems Techno.Com: Jurnal Teknologi Informasi Bulletin of Electrical Engineering and Informatics JUITA : Jurnal Informatika Proceeding of the Electrical Engineering Computer Science and Informatics Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Jurnal Informatika dan Rekayasa Elektronik Community Development Journal: Jurnal Pengabdian Masyarakat
Syafaah, Lailis
Universitas Muhammadiyah Malang
Humanities Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Health Professions Public Health

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Journal : Bulletin of Electrical Engineering and Informatics

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Diabetes prediction based on discrete and continuous mean amplitude of glycemic excursions using machine learning Lailis Syafaah; Setio Basuki; Fauzi Dwi Setiawan Sumadi; Amrul Faruq; Mauridhi Hery Purnomo
Bulletin of Electrical Engineering and Informatics Vol 9, No 6: December 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v9i6.2387

Abstract

Chronic hyperglycemia and acute glucose fluctuations are the two main factors that trigger complications in diabetes mellitus (DM). Continuous and sustainable observation of these factors is significant to be done to reduce the potential of cardiovascular problems in the future by minimizing the occurrence of glycemic variability (GV). At present, observations on GV are based on the mean amplitude of glycemic excursion (MAGE), which is measured based on continuous blood glucose data from patients using particular devices. This study aims to calculate the value of MAGE based on discrete blood glucose observations from 43 volunteer patients to predict the diabetes status of patients. Experiments were carried out by calculating MAGE values from original discrete data and continuous data obtained using Spline Interpolation. This study utilizes the machine learning algorithm, especially k-Nearest Neighbor with dynamic time wrapping (DTW) to measure the distance between time series data. From the classification test, discrete data and continuous data from the interpolation results show precisely the same accuracy value that is equal to 92.85%. Furthermore, there are variations in the MAGE value for each patient where the diabetes class has the most significant difference, followed by the pre-diabetes class, and the typical class. 
Co-Authors A.A. Ketut Agung Cahyawan W Agus Heriyanto Alvian Widianto Amrul Faruq Basuki, Setio Diding Suhardi Ilham Pakaya Iqbal Abadilah Umar Irfan, M. Irfan, Muhammad Khaeruddin Khaeruddin Khusnul Hidayat Lukman Hakim M. Irfan M. Irfan Machmud Effendy Mauridhi Hery Purnomo Merinda Lestandy Muhammad Ibnu Alfarizi Novendra Setyawan Nur Alif Nurhadi, Nurhadi Selfi Rizkianto Sumadi, Fauzi Dwi Setiawan Widianto Widianto Widianto Widianto Widianto, Widianto Yoga Nur Firmansyah Yudawan Hidayat