APPLYING MACHINE LEARNING TO PREDICT GESTATIONAL DIABETES
Abstract
Gestational diabetes is a type of diabetes diagnosed for the first time during pregnancy that can affect the health of pregnancy and fetus. Patients who have had gestational diabetes in the past have an increased risk of developing type 2 diabetes. Therefore, detecting and predicting the likelihood of gestational diabetes by machine learning is essential. This paper applies machine learning models such as Support Vector Machine (SVM), Decision Tree, Random Forest and Gradient Descent to predict pregnant women (gestational age from 24 to 28 weeks) whether you have gestational diabetes. Experimental results show that the accuracy of prediction is quite high, approximately from 93 % to more than 96 %. This will make it easier for clinicians to manage gestational diabetes, especially during the last trimester of pregnancy, when blood sugar levels are often high.
References
Coustan, Donald R. “Gestational diabetes mellitus”. Clinical chemistry, 2013, 59, no. 9, pp. 1310-1321.
Ye, Y., Xiong, Y., Zhou, Q., Wu, J., Li, X., & Xiao, X., Comparison of Machine Learning Methods and Conventional Logistic Regressions for Predicting Gestational Diabetes Using Routine Clinical Data: A Retrospective Cohort Study. Journal of Diabetes Research, 2020.
Pustozerov, E. A., Tkachuk, A. S., Vasukova, E. A., Anopova, A. D., Kokina, M. A., Gorelova, I. V., ... & Popova, P. V., Machine Learning Approach for Postprandial Blood Glucose Prediction in Gestational Diabetes Mellitus. IEEE Access, 2020, 8, pp. 219308-219321.
Xiong, Y., Lin, L., Chen, Y., Salerno, S., Li, Y., Zeng, X., & Li, H., Prediction of gestational diabetes mellitus in the first 19 weeks of pregnancy using machine learning techniques. The Journal of Maternal-Fetal & Neonatal Medicine, 2020, pp. 1-7.
Artzi, N. S., Shilo, S., Hadar, E., Rossman, H., Barbash-Hazan, S., Ben-Haroush, A., ... & Segal, E., Prediction of gestational diabetes based on nationwide electronic health records. Nature medicine, 2020, 26(1), pp. 71-76.
Wu, Y. T., Zhang, C. J., Mol, B. W., Kawai, A., Li, C., Chen, L., ... & Huang, H. F., Early prediction of gestational diabetes mellitus in the Chinese population via advanced machine learning. The Journal of Clinical Endocrinology & Metabolism, 2020.
Yoffe, L., Polsky, A., Gilam, A., Raff, C., Mecacci, F., Ognibene, A., ... & Hod, M., Early diagnosis of gestational diabetes mellitus using circulating microRNAs. European journal of endocrinology, 2019, 181(5), pp. 565-577.
Grus, J., Data science from scratch: first principles with python. O’Reilly Media, 2019.
Flach, Peter A., and Meelis Kull. “Precision-Recall-Gain Curves: PR Analysis Done Right.” In NIPS, vol. 15, 2015
Zheng, Tao, Wei Xie, Liling Xu, Xiaoying He, Ya Zhang, Mingrong You, Gong Yang, and You Chen. “A machine learning-based framework to identify type 2 dia-betes through electronic health rec-ords”. International journal of medical informatics, 2017, 97, pp. 120-127.
Dagliati, Arianna, Simone Marini, Lucia Sacchi, Giulia Cogni, Marsida Teliti, Val-entina Tibollo, Pasquale De Cata, Luca Chiovato, and Riccardo Bellazzi. “Machine learning methods to predict diabetes complications”. Journal of diabetes science and technology, 2018, 12, no. 2, pp. 295-302.
Leo Breiman, Random Forests. Machine Learning. October 2001, Volume 45, Issue 1, pp 5–32.
J. Friedman, “Greedy Function Approximation: A Gradient Boosting Machine”. The Annals of Statistics, 2001, Vol. 29, No. 5.
Polikar, R., “Ensemble based systems in decision making”. IEEE Circuits and Systems Magazine, 2006, 6 (3), pp. 21–45. doi:10.1109/MCAS.2006.1688199.
Rokach, L., “Ensemble-based classifiers”. Artificial Intelligence Review, 2010, 33 (1-2), pp. 1–39. doi:10.1007/s10462-009-9124-7.
Alexey Nefedov, Support Vectơ Machines: A Simple Tutorial. Creative Commons Attribution - NonCommercial - NoDerivatives 4.0 license, 2016.
