Prediction of glycaemic control and quality of life in people with type 2 diabetes using glucose-lowering drugs with machine learning—The Maastricht study

Abstract

Background

Despite the heterogeneity of type 2 diabetes (T2D), all patients are treated according to the same guideline. Some people have more difficulty reaching treatment goals (adequate glycaemic control) and maintaining quality of life (QoL). Therefore, a prediction model identifying who is unlikely to reach these goals within the next year would be useful to allow specific attention to these people.

Aim

To investigate if machine learning algorithms can predict which individuals are unlikely to reach glycaemic control and likely to deteriorate in QoL in 1 year.

Methods

We used data from The Maastricht Study, including 842 people with T2D and information on HbA1c values, and 964 people with T2D and information on QoL. We evaluated several machine learning algorithms with feature selection methods and hyperparameter tuning in fivefold cross-validation for the corresponding outcomes.

Results

The prediction of inadequate glycaemic control showed good performance. The support vector machine classifier performed best in terms of accuracy (0.76 (95% CI 0.71–0.79)), precision (0.79 (95% CI 0.71–0.83)) and area under the receiver operating characteristic curve (AUC-ROC) (0.85 (95% CI 0.80–0.89)). The multi-layer perceptron classifier performed best in terms of recall (0.72 (95% CI 0.64–0.79)) and F1-score (0.73 (95% CI 0.64–0.79)). The prediction of deterioration in QoL showed inadequate performance and did not seem feasible.

Conclusion

Prediction of glycaemic control after 1 year in T2D is feasible with good model performance. However, the prediction of deterioration in QoL remains a challenge and needs further work.