Human-In-The-Loop (HITL) application design for early detection of pregnancy danger signs


Human-In-The-Loop (HITL)
early detection
pregnancy danger signs
machine learning
mobile application

How to Cite

Widyawati, M. N., Astuti, E. H. P., & Kurnianingsih, K. (2022). Human-In-The-Loop (HITL) application design for early detection of pregnancy danger signs . Belitung Nursing Journal, 8(2), 161–168.
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Background: Pregnancy period is a period for mothers to empower themselves to be safe and comfortable. Pregnant women must acquire pregnancy-related information, such as warning signs of pregnancy, to avoid severe complications and even death during pregnancy and childbirth. Therefore, developing an application for pregnant women would be very helpful.

Objective: This study aimed to apply Human-In-The-Loop design with an android application to detect pregnancy risk early and avoid maternal morbidity and mortality.

Methods: We collected data from the cohort of 5324 pregnant women at the community health centers in the West Lombok District from 2020 to February 2021. The data included age, parity, height, inter-pregnancy interval, hemoglobin levels, upper arm circumference, previous diseases, and bleeding history. We developed a Human-In-The-Loop mobile application and employed the decision tree for identifying pregnancy danger signs. The midwife (human-in-the-loop) reviewed and clarified the data to generate the final detection and made a recommendation.

Results: The ordinal regression model revealed that older patients who have more parity, lower height, the distance of children <2 years, hemoglobin <11 g/dl, upper arm circumference (UPC) <23.5 cm, have positive HBsAg, have HIV disease, have a history of diabetes mellitus (DM), have a history of hypertension, positive protein urine, and have other diseases are more likely to have a high maternal risk. The decision tree outperformed and obtained a high accuracy of 92% ± 0.0351 compared to the nine individual classifiers (Nearest Neighbors, Random Forest, Neural Net, AdaBoost, Gaussian Naïve Bayes, Bagging, Extra Tree, Gradient Boosting, and Stacking).

Conclusion: The Human-In-The-Loop mobile app developed in this study can be used by healthcare professionals, especially midwives and nurses, to detect danger indications early in pregnancy, accurately diagnose the high risk of pregnancy, and provide treatment and care recommendations during pregnancy and childbirth.


Copyright (c) 2022 Melyana Nurul Widyawati, Ery Hadiyani Puji Astuti, Kurnianingsih Kurnianingsih

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This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

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Declaration of Conflicting Interest

The authors declared no potential conflicts of interest in this study.


We would like to thank the health officers in West Lombok and the volunteers for their willingness to participate in this study.

Authors’ Contributions

All authors contributed equally to manuscript preparation by collecting data, reviewing the literature, critically reviewing design and concepts, article drafting, and language editing. All authors approved the final version of the manuscript.

Data Availability

All data collected and analyzed during the conduct of this study are included in this published article. However, for data privacy, data or information are not publicly accessible or available to avoid compromise to the research participants.


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