Early Detection of Acute Coronary Syndrome Using a Mobile Digital Health Application.

Journal: Studies in health technology and informatics
Published Date: Aug 7, 2025

Abstract

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.

Authors

  • Mifetika Lukitasari
    School of Population Health, UNSW Sydney, NSW 2052 Australia.
  • Allen Lamarca Nazareno
    Institute of Mathematical Sciences, College of Arts and Sciences, University of the Philippines, Los Banos, Laguna, Philippines.
  • Mohammad Saifur Rohman
    Department of Cardiology and Vascular Medicine, Faculty of Medicine, Brawijaya University, Malang, Indonesia.
  • Jitendra Jonnagaddala
    School of Public Health and Community Medicine, University of New South Wales, Australia; Asia-Pacific Ubiquitous Healthcare Research Centre, University of New South Wales, Australia; Prince of Wales Clinical School, University of New South Wales, Australia.

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