Predicting the future risk of developing type 2 diabetes in women with a history of gestational diabetes mellitus using machine learning and explainable artificial intelligence.

Abstract

Background and aim: It is essential to identify the risk of developing Type 2 Diabetes Mellitus (T2DM) in women with a history of Gestational Diabetes Mellitus (GDM). This study seeks to create a machine learning (ML) model combined with explainable artificial intelligence (XAI) to predict and explain the risk of Type 2 Diabetes Mellitus (T2DM) in women with a history of Gestational Diabetes Mellitus (GDM).

Methods: A literature review found 28 risk factors, including pregnancy-related clinical risk factors, maternal characteristics, genetic risk factors, and lifestyle and modifiable risk factors. A synthetic dataset was generated utilizing subject expertise and clinical experience through Python programming. Various machine learning classification techniques were employed on the data to identify the optimal model, which integrates interpretability approaches (SHAP) to guarantee the transparency of model predictions.

Results: The developed machine learning model exhibited superior accuracy in predicting the risk of T2DM relative to conventional clinical risk scores, with notable contributions from factors such as insulin treatment during pregnancy, physical inactivity, obesity, breastfeeding, a history of recurrent GDM, an unhealthy diet, and ethnicity. Integrated XAI assists clinicians in comprehending the relevant risk factors and their influence on certain predictive outcomes.

Conclusions: Machine learning and explainable artificial intelligence provide a comprehensive methodology for individualized risk evaluation in women with a history of gestational diabetes mellitus. This methodology, by integrating extensive real-world data, offers healthcare clinicians actionable insights for early intervention.