Machine Learning Models for Predicting Risky Pregnancies in Early Clinical Interventions
Atik Shahariyar Hasan 1, Sree Shib Shankar Devnath Debu 2, Israt Jahan Eti 3, Md. Halimuzzaman 4, Mohammad Rezaul Karim 5, Birupaksha Biswas 6
Journal of Angiotherapy 8(8) 1-9 https://doi.org/10.25163/angiotherapy.889905
Submitted: 23 June 2024 Revised: 13 August 2024 Published: 15 August 2024
Abstract
Background: Risky pregnancies present significant challenges in maternal healthcare, often requiring accurate prediction to prevent adverse outcomes. Machine learning (ML) models offer a promising approach for predicting such risks, enabling timely interventions. This study evaluates five machine learning models—Logistic Regression, Decision Tree, K-Nearest Neighbors (KNN), Naive Bayes, and Support Vector Machine (SVM)—for their effectiveness in predicting risky pregnancies using clinical datasets. Methods: The study developed and evaluated five ML models, each implemented using Python’s scikit-learn library. The dataset was split into 75% for training and 25% for testing. Standard classification metrics, including accuracy, precision, recall, and F1-score, were used to assess model performance. Hyperparameter tuning was conducted using grid search and cross-validation to optimize model parameters. The models’ performance was compared to identify the most suitable for clinical applications. Results: The Decision Tree model achieved the highest accuracy (100% on training data, 95.6% on testing data), along with excellent precision, recall, and F1-scores for both classes, making it the most accurate and interpretable model for predicting risky pregnancies. Logistic Regression also performed well, particularly in identifying high-risk cases, with testing accuracy of 82%. KNN and SVM provided moderate accuracy, with KNN achieving 78% testing accuracy and SVM 80%. Naive Bayes, however, performed poorly, achieving only 43.2% accuracy due to its assumption of feature independence, which was not suitable for the dataset. Conclusion: The Decision Tree and Logistic Regression models emerged as the most effective for predicting risky pregnancies, offering high accuracy and interpretability, crucial for clinical decision-making.
Keywords: Risky pregnancies, machine learning, Decision Tree, Logistic Regression, predictive modeling
References
Altman, D. G. (1992). Practical statistics for medical research. Chapman & Hall.
Barker, D. J. P. (2007). The origins of the developmental origins theory. Journal of Internal Medicine, 261(5), 412-417.
Black, R. E., Victora, C. G., Walker, S. P., Bhutta, Z. A., Christian, P., de Onis, M., ... & Uauy, R. (2013). Maternal and child undernutrition and overweight in low-income and middle-income countries. The Lancet, 382(9890), 427-451.
Bodnar, L. M., & Wisner, K. L. (2015). Nutrition and depression: implications for improving mental health among childbearing-aged women. Biological Psychiatry, 77(9), 685-687.
Chowdhury, R., Doi, S. A. R., Gopalakrishnan, S., Lam, W. Y., & Sharma, M. (2016). Systematic review with meta-analysis: impact of preoperative nutritional supplementation on postoperative outcomes in gastrointestinal surgery. Journal of Clinical Epidemiology, 75, 1-13.
Darnton-Hill, I., Nishida, C., & James, W. P. T. (2019). A life course approach to diet, nutrition and the prevention of chronic diseases. Public Health Nutrition, 7(1A), 101-121.
Duarte, D. P., Maciel, L. G., & de Souza, R. J. (2019). Machine learning and nutritional status: The state of the art. Computers in Biology and Medicine, 104, 202-210.
Halimuzzaman, Md., Sharma, Dr. J., Bhattacharjee, T., Mallik, B., Rahman, R., Rezaul Karim, M., Masrur Ikram, M., & Fokhrul Islam, M. (2024). Blockchain Technology for Integrating Electronic Records of Digital Healthcare System. Journal of Angiotherapy, 8(7). http://publishing.emanresearch.org/Journal/Abstarct/angiotherapy.879740
Hsieh, P. C., Wu, C. Z., & Chen, Y. C. (2018). Utilization of wearable devices for maternal and fetal health care: a systematic review. Nursing Research, 67(5), 401-408.
Khalilia, M., Chakraborty, S., & Popescu, M. (2011). Predicting disease risks from highly imbalanced data using random forest. BMC Medical Informatics and Decision Making, 11(1), 51.
Lassi, Z. S., Moin, A., & Bhutta, Z. A. (2013). Nutrition in pregnancy: developing country perspectives. Seminars in Fetal and Neonatal Medicine, 18(6), 378-384.
Liu, Y., Chen, P. H. C., & Krause, J. (2020). How to read articles that use machine learning: users’ guides to the medical literature. JAMA, 322(18), 1800-1809.
Obermeyer, Z., & Emanuel, E. J. (2016). Predicting the future—big data, machine learning, and clinical medicine. The New England Journal of Medicine, 375(13), 1216-1219.
Rajkomar, A., Dean, J., & Kohane, I. (2019). Machine learning in medicine. The New England Journal of Medicine, 380(14), 1347-1358.
Subar, A. F., Freedman, L. S., Tooze, J. A., Kirkpatrick, S. I., Boushey, C., Neuhouser, M. L., ... & Kipnis, V. (2015). Addressing current criticism regarding the value of self-report dietary data. Journal of Nutrition, 145(12), 2639-2645.
Thompson, W. R., Gordon, N. F., & Pescatello, L. S. (2019). ACSM's guidelines for exercise testing and prescription. Lippincott Williams & Wilkins.
Vellido, A., Martín-Guerrero, J. D., & Lisboa, P. J. G. (2012). Making machine learning models interpretable. Proceedings of the European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, 21, 163-172.
Zhang, Z., Chen, X., Zhu, Q., Li, L., Zhao, H., & Jin, B. (2020). Predicting pregnancy complications with machine learning models based on maternal health monitoring data. European Journal of Obstetrics & Gynecology and Reproductive Biology, 252, 165-170.
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