TY - GEN
T1 - Feature Selection of Photoplethysmograph Data in Machine Learning
AU - Haq, Faris Atoil
AU - Sarno, Riyanarto
AU - Abdillah, Rifqi
AU - Amri, Taufiq Choirul
AU - Septiyanto, Abdullah Faqih
AU - Sungkono, Kelly Rossa
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - Photoplethysmography signals are more responsive to changes in blood volume, not vascular pressure. Nowadays, more and more research is being developed for medical purposes, one of which is to diagnose diseases through fingertip pulse waves. This study proposes a new approach to optimize the statistical parameters of regression produced by PPG signals. The fingertip pulse wave device samples the PPG signal in humans and obtains the value of the signal. By taking the following samples, through processing using machine learning to process PPG signal data. machine learning is built to process PPG signal parameter data by the proposed method. The machine learning of feature selection algorithm that used are Forward Feature Selection Algorithm (FFS) and Sequential Input Selection Algorithm (SISAL).
AB - Photoplethysmography signals are more responsive to changes in blood volume, not vascular pressure. Nowadays, more and more research is being developed for medical purposes, one of which is to diagnose diseases through fingertip pulse waves. This study proposes a new approach to optimize the statistical parameters of regression produced by PPG signals. The fingertip pulse wave device samples the PPG signal in humans and obtains the value of the signal. By taking the following samples, through processing using machine learning to process PPG signal data. machine learning is built to process PPG signal parameter data by the proposed method. The machine learning of feature selection algorithm that used are Forward Feature Selection Algorithm (FFS) and Sequential Input Selection Algorithm (SISAL).
KW - Fast Forward Selection
KW - Feature Selection
KW - PPG
KW - Sequential Input Selection Algorithm
UR - http://www.scopus.com/inward/record.url?scp=85152045741&partnerID=8YFLogxK
U2 - 10.1109/ICAIIC57133.2023.10067116
DO - 10.1109/ICAIIC57133.2023.10067116
M3 - Conference contribution
AN - SCOPUS:85152045741
T3 - 5th International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2023
SP - 315
EP - 320
BT - 5th International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2023
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 5th International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2023
Y2 - 20 February 2023 through 23 February 2023
ER -