Predicting short‐ and long‐term glycated haemoglobin response after insulin initiation in patients with type 2 diabetes mellitus using machine‐learning algorithms

AIM: To assess the potential of supervised machine‐learning techniques to identify clinical variables for predicting short‐term and long‐term glycated haemoglobin (HbA1c) response after insulin treatment initiation in patients with type 2 diabetes mellitus (T2DM). MATERIALS AND METHODS: We included patients with T2DM from the Groningen Initiative to Analyse Type 2 diabetes Treatment (GIANTT) database who started insulin treatment between 2007 and 2013 and had a minimum follow‐up of 2 years. Short‐ and long‐term responses at 6 (±2) and 24 (±2) months after insulin initiation, respectively, were assessed. Patients were defined as good responders if they had a decrease in HbA1c ≥ 5 mmol/mol or reached the recommended level of HbA1c ≤ 53 mmol/mol. Twenty‐four baseline clinical variables were used for the analysis and an elastic net regularization technique was used for variable selection. The performance of three traditional machine‐learning algorithms was compared for the prediction of short‐ and long‐term responses and the area under the receiver‐operating characteristic curve (AUC) was used to assess the performance of the prediction models. RESULTS: The elastic net regularization‐based generalized linear model, which included baseline HbA1c and estimated glomerular filtration rate, correctly classified short‐ and long‐term HbA1c response after treatment initiation, with AUCs of 0.80 (95% CI 0.78–0.83) and 0.81 (95% CI 0.79–0.84), respectively, and outperformed the other machine‐learning algorithms. Using baseline HbA1c alone, an AUC = 0.71 (95% CI 0.65–0.73) and 0.72 (95% CI 0.66–0.75) was obtained for predicting short‐term and long‐term response, respectively. CONCLUSIONS: Machine‐learning algorithm performed well in the prediction of an individual's short‐term and long‐term HbA1c response using baseline clinical variables.