TY - GEN
T1 - Early Detection of Acute Coronary Syndrome Using a Mobile Digital Health Application
AU - Lukitasari, Mifetika
AU - Nazareno, Allen Lamarca
AU - Rohman, Mohammad Saifur
AU - Jonnagaddala, Jitendra
N1 - Publisher Copyright:
© 2025 The Authors.
PY - 2025/8/7
Y1 - 2025/8/7
N2 - Early detection of acute coronary syndrome (ACS) is vital for reducing ischemic time and preserving more heart muscle.Chest pain is the most common symptom of acute coronary syndrome (ACS). This study used a quick chest pain assessment questionnaire embedded in the DETAK mobile application to predict ACS. Data from 566 patients (412 with ACS and 154 without ACS) were analysed. Cardiologists confirmed the diagnosis of acute coronary syndrome (STEMI and NSTEMI). Patients completed the questionnaire, developed by expert consensus, within 48 hours of admission or transfer. Random forest machine learning, using Python version 3.12.4, was utilized to predict ACS. The model achieved an accuracy of 0.81, precision of 0.86, recall of 0.9, specificity of 0.54, and an F1-score of 0.88. This simple and quick assessment using DETAK shows the potential for scaling up the early detection of ACS in a broader community.
AB - Early detection of acute coronary syndrome (ACS) is vital for reducing ischemic time and preserving more heart muscle.Chest pain is the most common symptom of acute coronary syndrome (ACS). This study used a quick chest pain assessment questionnaire embedded in the DETAK mobile application to predict ACS. Data from 566 patients (412 with ACS and 154 without ACS) were analysed. Cardiologists confirmed the diagnosis of acute coronary syndrome (STEMI and NSTEMI). Patients completed the questionnaire, developed by expert consensus, within 48 hours of admission or transfer. Random forest machine learning, using Python version 3.12.4, was utilized to predict ACS. The model achieved an accuracy of 0.81, precision of 0.86, recall of 0.9, specificity of 0.54, and an F1-score of 0.88. This simple and quick assessment using DETAK shows the potential for scaling up the early detection of ACS in a broader community.
KW - Acute coronary syndrome
KW - Chest pain assessment
KW - Digital health
KW - Early detection
UR - https://www.scopus.com/pages/publications/105013169687
U2 - 10.3233/SHTI251019
DO - 10.3233/SHTI251019
M3 - Conference contribution
C2 - 40776037
AN - SCOPUS:105013169687
T3 - Studies in Health Technology and Informatics
SP - 1150
EP - 1154
BT - MEDINFO 2025 - Healthcare Smart x Medicine Deep
A2 - Househ, Mowafa S.
A2 - Househ, Mowafa S.
A2 - Tariq, Zain Ul Abideen
A2 - Al-Zubaidi, Mahmood
A2 - Shah, Uzair
A2 - Huesing, Elaine
PB - IOS Press BV
T2 - 20th World Congress on Medical and Health Informatics, MEDINFO 2025
Y2 - 9 August 2025 through 13 August 2025
ER -