Evaluation of factors predicting transition from prediabetes to diabetes among patients residing in underserved communities in the United States - A machine learning approach.

INTRODUCTION

Over one-third of the population in the United States (US) has prediabetes. Unfortunately, underserved population in the United States face a higher burden of prediabetes compared to urban areas, increasing the risk of stroke and heart disease. There is a gap in the literature in understanding early predictors of diabetes among patients with prediabetes living in underserved communities in the United States. Hence, this study's objective is to identify factors influencing the transition from prediabetes to diabetes in rural or underserved communities using a machine learning approach.

METHODS

We conducted a retrospective analysis of data from prediabetic patients between 2012 and 2022. Eligible participants were at least 18 years old with baseline HbA1c levels between 5.7 % and 6.4 %. Eleven machine learning algorithms were evaluated using ten-fold cross-validation, including Logistic Regression (LR), Support Vector Classifier (SVC), K-nearest Neighbor (KNN), Gaussian Naive Bayes (GaussianNB), Bernoulli Naive Bayes (BernoulliNB), Adaptive Boosting (AdaBoost), Decision Tree (DT), Random Forest (RF), Gradient Boosting (GB), Extreme Gradient Boosting (XGBoost), and Extra Trees (ET). Subsequently, the SHAP framework was used to assess predictor influence and interactions observed with the top model.

RESULTS

Out of 5816 patients, 1910 met the criteria, with 426 progressing to diabetes. The Random Forest model achieved the highest accuracy (90.0 %) and AUC (0.963), followed by Extra Trees (89.5 % accuracy, AUC 0.962) and XGBoost (88.6 % accuracy, AUC 0.952). Logistic Regression demonstrated lower performance but outperformed other models such as K-Nearest Neighbors and Gaussian Naive Bayes. SHAP analysis with the RF model identified key predictors and their interactions. A significant interaction showed that lower BMI values, combined with increasing age, were associated with a reduced risk of diabetes progression, while higher BMI at younger ages increased the likelihood of progression. Additionally, several social determinants of health were identified as significant predictors.

CONCLUSION

Among the 11 models, the Random Forest model showed the strongest reliability for predicting diabetes progression. The results of this study can be used to inform public policy implications for the development of early, targeted interventions focusing on social determinants of health, dietary counseling, and BMI management to prevent diabetes in underserved communities.