A machine learning approach to predicting severe diabetes distress in adults with type 1 diabetes mellitus.

BACKGROUND

Diabetes distress is common in patients with type 1 diabetes mellitus (T1DM). The aim of this study was to construct and validate prediction models for diabetes distress in adults with T1DM using continuous glucose monitoring (CGM) metrics.

METHODS

The CGM metrics were collected from 259 adults with T1DM. Severe diabetes distress was defined as 40 points on the Problem Areas in Diabetes scale. Prediction models were developed based on ten machine learning algorithms: random forest (RF), support vector machine (SVM), Naive Bayes (NB), Neural Network (NN), k-nearest neighbor (k-NN), XGBoost (XGB), SGDClassifier (SGDC), XGB_limitet_depth (CGB_ld), L1LogisticRegression (L1), and LightGBM. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC), accuracy, precision, recall, and F1 score.

RESULTS

Among the ten models, accuracy in the NN model was the highest (NN: 0.744, L1: 0.731, NB: 0.718, SVM: 0.692, SGDC: 0.654, RF: 0.628, k-NN: 0.628, LightGBM: 0.615, XGBoost: 0.564, and XGB_ld: 0.564). The NN model achieved the highest AUC of 0.728 (95% confidence interval: 0.608-0.845).

CONCLUSIONS

This study developed a predictive model for severe diabetes distress using machine learning, incorporating both demographic and CGM metrics in adults with type 1 diabetes mellitus. The NN model demonstrated potential as a practical tool to assist clinicians in identifying individuals at risk of severe diabetes distress.