Machine Learning-Based Maternal Health Risk Assessment: A Comparative Analysis of Classification Algorithms for Predicting Risk Levels During Pregnancy
DOI:
https://doi.org/10.6000/1929-6029.2025.14.53Keywords:
Maternal health, risk prediction, machine learning, pregnancy complications, healthcare analyticsAbstract
Background: Maternal health risk assessment remains a critical challenge in healthcare, particularly in resource-limited settings where early identification of high-risk pregnancies can significantly impact maternal and fetal outcomes. This study evaluates the performance of multiple machine learning algorithms for predicting maternal health risk levels using physiological parameters.
Methods: We analyzed a dataset of 1014 pregnant women from Kaggle, incorporating six key features: age, systolic blood pressure, diastolic blood pressure, blood sugar levels, body temperature, and heart rate. Risk levels were classified as mild (0), moderate (1), and severe (2). Four machine learning algorithms were implemented and compared: Logistic Regression, Random Forest, Support Vector Machine (SVM), and K-Nearest Neighbors (KNN).
Results: Random Forest and SVM achieved perfect classification performance with 100% accuracy, precision, recall, and F1-scores across all risk categories. Logistic Regression demonstrated strong performance with 98% overall accuracy, showing minor challenges in recall for moderate risk cases (93%). KNN achieved 98% accuracy with balanced performance across risk categories, though slightly lower precision for mild risk cases (95%).
Conclusion: Machine learning algorithms, including Random Forest and SVM, show promise in predicting maternal health risks; however, further validation across diverse populations is essential before clinical adoption.
References
World Health Organization. Maternal mortality: key facts. Geneva: WHO 2023.
Smith GCS, Pell JP, Dobbie R. Interpregnancy interval and risk of preterm birth and neonatal death: retrospective cohort study. BMJ 2003; 327(7410): 313-8. DOI: https://doi.org/10.1136/bmj.327.7410.313
Khan KS, Wojdyla D, Say L, Gülmezoglu AM, Van Look PF. WHO analysis of causes of maternal death: a systematic review. Lancet 2006; 367(9516): 1066-74. DOI: https://doi.org/10.1016/S0140-6736(06)68397-9
Goldenberg RL, McClure EM, MacGuire ER, Kamath BD, Jobe AH. Lessons for low-income regions following the reduction in hypertension-related maternal mortality in high-income countries. Int J Gynaecol Obstet 2011; 113(2): 91-5. DOI: https://doi.org/10.1016/j.ijgo.2011.01.002
Ronsmans C, Graham WJ. Lancet Maternal Survival Series Steering Group. Maternal mortality: who, when, where, and why. Lancet 2006; 368(9542): 1189-200. DOI: https://doi.org/10.1016/S0140-6736(06)69380-X
Campbell OMR, Graham WJ. Lancet Maternal Survival Series Steering Group. Strategies for reducing maternal mortality: getting on with what works. Lancet 2006; 368(9543): 1284-99. DOI: https://doi.org/10.1016/S0140-6736(06)69381-1
Rajkomar A, Dean J, Kohane I. Machine learning in medicine. N Engl J Med 2019; 380(14): 1347-58. DOI: https://doi.org/10.1056/NEJMra1814259
Obermeyer Z, Emanuel EJ. Predicting the future: big data, machine learning, and clinical medicine. N Engl J Med 2016; 375(13): 1216-9. DOI: https://doi.org/10.1056/NEJMp1606181
Chen JH, Asch SM. Machine learning and prediction in medicine: beyond the peak of inflated expectations. N Engl J Med 2017; 376(26): 2507-9. DOI: https://doi.org/10.1056/NEJMp1702071
Topol EJ. High-performance medicine: the convergence of human and artificial intelligence. Nat Med 2019; 25(1): 44-56. DOI: https://doi.org/10.1038/s41591-018-0300-7
Ahmed M. Maternal health risk [dataset]. UCI Machine Learning Repository; 2020.
Breiman L. Random forests. Mach Learn 2001; 45(1): 5-32. DOI: https://doi.org/10.1023/A:1010933404324
Cortes C, Vapnik V. Support-vector networks. Mach Learn 1995; 20(3): 273-97. DOI: https://doi.org/10.1007/BF00994018
Dietterich TG. Ensemble methods in machine learning. Mult Classif Syst 2000; 1857: 1-15. DOI: https://doi.org/10.1007/3-540-45014-9_1
Liaw A, Wiener M. Classification and regression by randomForest. R News 2002; 2(3): 18-22.
Hastie T, Tibshirani R, Friedman J. The elements of statistical learning: data mining, inference, and prediction. 2nd ed. New York: Springer 2009. DOI: https://doi.org/10.1007/978-0-387-84858-7
Cristianini N, Shawe-Taylor J. An introduction to support vector machines and other kernel-based learning methods. Cambridge: Cambridge University Press 2000. DOI: https://doi.org/10.1017/CBO9780511801389
Vapnik VN. Statistical learning theory. New York: Wiley; 1998.
Cover T, Hart P. Nearest neighbor pattern classification. IEEE Trans Inf Theory 2006; 13(1): 21-7. DOI: https://doi.org/10.1109/TIT.1967.1053964
Hosmer DW, Lemeshow S, Sturdivant RX. Applied logistic regression. 3rd ed. Hoboken: Wiley 2013. DOI: https://doi.org/10.1002/9781118548387
Altman N, Krzywinski M. The curse(s) of dimensionality. Nat Methods 2018; 15(6): 399-400. DOI: https://doi.org/10.1038/s41592-018-0019-x
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Policy for Journals/Articles with Open Access
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are permitted and encouraged to post links to their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work
Policy for Journals / Manuscript with Paid Access
Authors who publish with this journal agree to the following terms:
- Publisher retain copyright .
- Authors are permitted and encouraged to post links to their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work .
