Machine Learning Based Prediction of Health Risks in Pregnant Women
DOI:
https://doi.org/10.62712/juktisi.v5i1.766Keywords:
machine learning, pregnancy risk, random forest, Classification, Maternal HealthAbstract
Pregnancy is an important phase that requires optimal health monitoring to prevent complications that are risky for both mother and fetus. The high maternal mortality rate in Indonesia emphasizes the importance of early detection of pregnancy risks. The use of machine learning offers an effective predictive approach to quickly and accurately identify pregnancy risks. This study aims to compare the performance of five machine learning algorithms, namely Logistic Regression, Decision Tree C4.5, Random Forest, Support Vector Machine, and Naive Bayes, using the Maternal Health Risk Dataset. The hold-out validation method with data sharing of 80% training data and 20% test data was used in this study. Model evaluation is conducted based on accuracy, precision, recall, and F1-score metrics. The results showed that Random Forest had the best performance with an accuracy of 93%, followed by Decision Tree at 93%, SVM at 82%, Logistic Regression at 76%, and Naive Bayes at 72%. Thus, Random Forest is rated as the most optimal algorithm in predicting pregnancy risk and potentially supporting the development of decision support systems for health workers. This research is expected to be the basis for the development of a machine learning-based decision support system to increase the effectiveness of health services for pregnant women.
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References
B. G. Sadikin, “Laporan Kinerja Kementrian Kesehatan RI,” Lap. Kinerja Kementrian Kesehat. RI, pp. 1–23, 2023.
N. Modi and Y. Kumar, “Automated Machine Learning-Based System for the Prediction of Maternal Health Indicators and High-Risk Pregnancy,” in 2025 7th International Conference on Energy, Power and Environment (ICEPE), IEEE, 2025, pp. 1–6.
K. Tomar, C. M. Sharma, and T. Prasad, “A Machine Learning-Based Risk Prediction Model During Pregnancy in Low-Resource Settings †,” pp. 1–9, 2024.
A. Hennessy, T. H. Tran, S. N. Sasikumar, and Z. Al-Falahi, “Machine learning, advanced data analysis, and a role in pregnancy care? How can we help improve preeclampsia outcomes?,” Pregnancy Hypertens., vol. 37, no. March, p. 101137, 2024, doi: 10.1016/j.preghy.2024.101137.
R. A. F. W. A. H. W. K. Rahman, “Analisis Perbandingan Algoritma Machine Learning untuk Klasifikasi Tingkat Risiko Ibu Hamil,” Student Res. J., vol. 1, no. 6, pp. 246–261, 2023.
R. Raja, I. Mukherjee, and B. K. Sarkar, “A Machine Learning-Based Prediction Model for Preterm Birth in Rural India,” J. Healthc. Eng., vol. 2021, 2021, doi: 10.1155/2021/6665573.
S. S. Assaduzzaman, Al Mashrafi, L. Tafakori, and M. Abdollahian, “Predicting maternal risk level using machine learning models,” BMC Pregnancy Childbirth, vol. 24, no. 1, 2024, doi: 10.1186/s12884-024-07030-9.
K. E. Setiawan, A. Kurniawan, and S. Y. Prasetyo, “Comparative analysis of machine learning decision tree-based Comparative analysis of machine learning decision models for predicting maternal health risks models for predicting maternal,” Procedia Comput. Sci., vol. 245, pp. 57–64, 2024, doi: 10.1016/j.procs.2024.10.229.
V. Khoirunnisa and S. Lestari, “Implementasi Klasifikasi Kehamilan Beresiko Dengan Metode Naive Bayes Pada Puskesmas Kelurahan Malaka Jaya,” J. Indones. Manaj. Inform. dan Komun., vol. 4, no. 3, pp. 1680–1693, 2023, doi: 10.35870/jimik.v4i3.396.
WHO, Trends in maternal mortality 2000 to 2020: estimates by WHO, UNICEF, UNFPA, World Bank Group and UNDESA/Population Division. 2023. [Online]. Available: https://www.who.int/publications/i/item/9789240068759
D. Hawale, A. Chavan, D. Timalsina, and A. B. Thatere, “The Role of Artificial Intelligence in Healthcare: A Review,” AIP Conf. Proc., vol. 3188, no. 1, pp. 1–22, 2024, doi: 10.1063/5.0240194.
World Health Organization 2013, “Diagnostic Criteria and Classification of Hyperglycaemia First Detected in Pregnancy,” pp. 1–62.
S. Khan et al., “Preeclampsia and eclampsia-specific maternal mortality in Bangladesh : Levels , trends , timing , and care-seeking practices,” vol. 13, pp. 1–12, 2023, doi: 10.7189/jogh.13.07003.
M. Sandri and P. Zuccolotto, “A bias correction algorithm for the gini variable importance measure in classification trees,” J. Comput. Graph. Stat., vol. 17, no. 3, pp. 611–628, 2008, doi: 10.1198/106186008X344522.
L. Saitta, “Support-Vector Networks,” vol. 297, pp. 273–297, 1995.
A. J. Scott, D. W. Hosmer, and S. Lemeshow, Applied Logistic Regression., vol. 47, no. 4. 1991. doi: 10.2307/2532419.
M. Ghanem et al., “brain sciences Limitations in Evaluating Machine Learning Models for Imbalanced Binary Outcome Classification in Spine Surgery : A Systematic Review,” 2023.
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