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Journal : Building of Informatics, Technology and Science

A Study of Feature Selection Method to Detect Coronary Heart Disease (CHD) on Photoplethysmography (PPG) Signals Faizal Akbari Putra; Satria Mandala; Miftah Pramudyo
Building of Informatics, Technology and Science (BITS) Vol 4 No 2 (2022): September 2022
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v4i2.2259

Abstract

Coronary Heart Disease (CHD) is a condition in which the heart's blood supply is blocked or disrupted by fat in the coronary arteries. This disease is the most significant cause of death in Indonesia. CHD can be detected based on the Heart Rate Variability (HRV) index of the Photophletysmograph (PPG) signal taken from a smartphone's camera. However, the use of PPG from smartphone to detect CHD is still rare in real-world applications. Moreover, studies on CHD detection based on PPG signal are also difficult to be found in the scientific literature. Currently, the Electrocardiogram (ECG) signal still dominates as a signal for detecting CHD. This research fills this research gap by proposing a study on the feature selection of PPG signal to detect CHD. There are three feature selection methods studied in this research, i.e., Analysis of Variance (Anova), Pearson Correlation, and Recursive Feature Elimination (RFE). Furthermore, a classification algorithm, called as K-Nearest Neighbors, has also been chosen to create a machine learning model based on the PPG features. The experimental results show that the Pearson Correlation feature selection method produces better CHD detection performance compared to the other two algorithms (Anova and RFE). CHD detection performance using the Pearson Correlation produces an accuracy of 90.9%, sensitivity of 75%, and specificity of 100%.
Co-Authors Abd. Rasyid Syamsuri Abdul Karim Adiwijaya Adly, Muhammad Ihsan Agus Alex Yanuar Akbar, Muh Aldebaran Bayu Nugroho Alwan, Maryam Hameed Andika Nugroho Putra Andreas Jonathan Silaban Annas, Aswar Ardian Rizal Arifin, Rezki Fauzan Ashydiki Malik Asmi Citra Malina, Asmi Citra Assir, Andi Azha Alvin Rahmansyah Bilal Ibrahim Bakri Budi Santosa Burchanuddin, Andi Eko Darwiyanto Endro Ariyanto Erwid Jadied Mustofa Erwid Mustofa Jadied Erwied M. Jadied Faiz Rofi Hencya Faizal Akbari Putra Fenty Alia Fikry, Maurice Fityanul Akhyar Hameed, Zainab Hasanuddin Hasanuddin Hidayatus Sholikhin Ilham Alimuddin, Ilham Indrayuni, Armi Irma Ruslina Defi Jumadil, Jumadil Juni, Juni Kusuma Adi Achmad Ledya Novamizanti M Jadied, Erwid Marmin, Hidayat Martin Halomoan Tamba Maurice Fikry Miftah Pramudyo Miftah Pramudyo Miftah Pramudyo Ming, Eileen Su Lee Mochamad Reza, Dandi Mohd Fadzil Hasssan Mohd Shahrizal Sunar Mohd Soperi Mohd Zahid Muhajir, Humaidid Muhammad Alif Akbar Muhammad Aniq Wafa Muhammad Hablul Barri Muhammad Ihsan Adly Muhammad Qomaruddin Muhammad Rafli Ramadhan Muhammad Yaumil Ihza Ihza Musfirah, Andi Nadia Ariana Nia Madu Marliana Niken Dwi Wahyu Cahyani Nugroho, Kahargyan Ario Pramudyo, Miftah Putu Harry Gunawan Raey Faldo Rafael Sebastian Rafi Ullah Rafly Athalla Ramadhan, Yusril Rifqi Syafiq Hibatul Aziz Rino Andias Anugraha Rio Guntur Utomo Rita Purnamasari Rizal, Ardian Salim M. Zaki Sobirin Sobirin Sutiyo Suyanto Suyanto Tassakka, Asmi Citra Malina A.R. Tong Boon Tang Tora Fahrudin Vera Suryani Wael M.S. Yafooz Wiyono Sutari Yafooz, Wael M.S. Yuan Wen Hau Yusril Ramadhan Zaki, Salim M. Zelmi Muhammad Adjel