Prediction of Type 2 Diabetes Mellitus According to Glucose Metabolism Patterns in Pregnancy Using a Novel Machine Learning Algorithm

• Published in: Journal of medical and biological engineering 2022-02, Vol.42 (1), p.138-144
• Main Authors: Houri, Ohad, Gil, Yotam, Chen, Rony, Wiznitzer, Arnon, Hochberg, Alyssa, Hadar, Eran, Berezowsky, Alexandra
• Format: Article
• Language: English
• Subjects: Age; Algorithms; Biological Techniques; Biomedical and Life Sciences; Biomedical Engineering and Bioengineering; Biomedical Engineering/Biotechnology; Biomedicine; Birth weight; Decision trees; Diabetes; Diabetes mellitus; Diabetes mellitus (non-insulin dependent); Fetuses; Gestational age; Glucose; Glucose metabolism; Glucose tolerance; Health care facilities; Learning algorithms; Machine learning; Medical records; Neonates; Original Article; Parameters; Patients; Prediction models; Pregnancy; Regenerative Medicine/Tissue Engineering
• ISSN: 1609-0985; 2199-4757
• DOI: 10.1007/s40846-022-00685-9

Purpose We aimed to predict future development of type-2 diabetes mellitus (T2DM) in women according to their glucose screening test results during pregnancy and several maternal and fetal characteristics, using a machine learning algorithm. Methods Machine learning was used to predict future development of T2DM following pregnancy in women who delivered at our university-affiliated tertiary medical center between 2007 to 2014. Data was retrieved from women’s medical records, and included: age, parity, gravidity, oral glucose tolerance test results—both the 1-h, 50-g, glucose challenge test (GCT) and the 3-h, 100-g, oral glucose tolerance test (OGTT), gestational age at delivery and birthweight. We used an XGBoost algorithm that fits the training data using decision trees. Results Six thousand and ninety-two women who had both GCT and OGTT data available were reviewed. We demonstrated an accuracy rate of 91% in predicting future T2DM, with a specificity and sensitivity of 74% each. The prediction model achieved an area under the curve of 0.85. The most predictive parameters were patient’s age at the index pregnancy and neonatal birthweight. Conclusions A state-of-the-art machine learning algorithm presents promising ability to predict T2DM following pregnancy using simple parameters such as GCT and OGTT values. The algorithm provides an opportunity to identify at-risk patients who may benefit from early assessment and intervention.