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All Journal International Journal of Basic and Applied Science Jurnal Media Informatika JUKESHUM: Jurnal Pengabdian Masyarakat Jurnal EduHealth
Handayani, Purnama
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Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Health Professions Nursing Physics Public Health Social Sciences Veterinary

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Journal : International Journal of Basic and Applied Science

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Dynamic model for early detection of preterm labor Nugraeny, Lolita; Suhartini, Suhartini; Sumiatik, Sumiatik; Handayani, Purnama
International Journal of Basic and Applied Science Vol. 13 No. 4 (2025): Computer Science, Engineering, Basic and Applied mathematics Science
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/ijobas.v13i4.678

Abstract

Preterm labor is a major challenge in maternal and neonatal health because it contributes to high rates of newborn morbidity and mortality. Early detection is crucial, but conventional static approaches often fail to identify risks accurately and in a timely manner. This study proposes the development of a dynamic machine learning-based preterm birth risk prediction model using the Long Short-Term Memory (LSTM) architecture combined with the Bayesian Updating approach. The model is designed to process multivariate time-series data from various clinical sources such as EHR (electronic medical record), EHG (electrohysterography), CTG (cardiotocography), and vital signals collected longitudinally during pregnancy. By leveraging LSTM's ability to capture long-term temporal relationships and Bayesian probabilistic renewal mechanisms, the model is able to provide real-time and adaptive estimates of preterm labor risk on a weekly basis. Risk prediction results are visualized in the form of interactive graphs with risk categorization (low, medium, high) to support fast and accurate clinical interpretation. The study used simulated data on 500 pregnant patients and showed that the system can adjust risk predictions as new data comes in. This research makes a significant contribution to the development of artificial intelligence-based clinical decision support systems for pregnancy monitoring. Going forward, integration with real clinical data and external validation in the hospital environment is expected to improve the accuracy and implementability of the system in daily medical practice.
Co-Authors Aghsila Hasma Marbun Anita Damayanti Lubis Dynaria Siregar, Yetti Fitriani Mediastuti Noviyanti Harahap, Rizky Nugraeny, Lolita Sari Harahap, Asrika Suhartini Suhartini Sumiatik, Sumiatik sunartono sunartono Ulpa Ismail, Ilvira