Article Per Year (5 Year)
p-Index From 2021 - 2026
0.408
P-Index
This Author published in this journals
All Journal JURNAL MEDIA INFORMATIKA BUDIDARMA Building of Informatics, Technology and Science
Miftah Pramudyo
Universitas Padjadjaran, Bandung
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Study of Classification Method to Detect Coronary Heart Disease Based On Signal Photoplethysmography (PPG) Azha Alvin Rahmansyah; Satria Mandala; Miftah Pramudyo
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 6, No 4 (2022): Oktober 2022
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v6i4.4823

Abstract

Coronary heart disease (CHD) is one of the deadliest diseases in the world, especially in Indonesia. This disease is caused by the accumulation of fat in blood vessels and can cause heart attacks that can endanger a person's health and safety. There are several methods for detecting CAD, such as using Electrocardiogram (ECG) signals and Photophlethysmograph (PPG) signals. However, studies that have tested machine learning classification methods to detect CAD using PPG signals are rarely found compared to detection using ECG. This study uses PPG signals taken from smartphone cameras to detect CHD, so that CHD detection is easier and affordable. To be able to diagnose CHD, machine learning assistance is needed to determine whether CHD is positive or negative. This study proposes a classification algorithm study to detect CAD. There are 3 classification methods used in this study. The three methods are KNN, SVM, and decision tree. The final results obtained in this study resulted in the best classification for KNN 81%, SVM 90%, and Decision Tree 90%. Each classification used has been carried out before and after tuning
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 Azha Alvin Rahmansyah Faizal Akbari Putra Satria Mandala