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Sri Rahayu
Department Master of Midwifery, Faculty of Health, Aisyah University, Pringsewu 35372, Indonesia
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management

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Software to predict maternal and child health risks with machine learning Fitriana Fitriana; Zulkifli Zulkifli; Sri Rahayu
Journal of Applied Data Sciences Vol 7, No 1: January 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i1.1088

Abstract

Objective: Maternal healthcare services are essential in public health, prioritizing the health and well-being of women throughout pregnancy, childbirth, as well as the postpartum period. The services include various efforts to safeguard the health of both the mother and the unborn child. During these stages, mothers face numerous risks and complications, making early risk detection critical for ensuring the safety of the pregnancy. Method: A novel method is needed that enables more accurate and affordable screening to improve early detection as well as increase maternal and child healthcare. Therefore, this study aimed to propose a solution including the development of software that uses the Naive Bayes algorithm to predict maternal and child health risks. The perceptions provided by the application served as an initial diagnostic reference for both expectant mothers and healthcare providers, offering a cost-effective as well as precise alternative. Result: During the analysis, the Naive Bayes algorithm was compared with Neural Network (NN) and Random Forest (RF) models to evaluate the prediction accuracy. Among the models used, NN produced the lowest accuracy at 48%. Conclusion: The estimated cost for developing this application was IDR 1,635,913.
Co-Authors Fitriana Fitriana Zulkifli Zulkifli