NIRCa: An artificial neural network-based insulin resistance calculator.

BACKGROUND

Direct measurement of insulin sensitivity in children with type 1 diabetes is cumbersome and time consuming.

OBJECTIVE

The aim of our study was to develop novel, accurate machine learning-based methods of insulin resistance estimation in children with type 1 diabetes.

METHODS

A hyperinsulinemic hyperglycemic clamp study was performed to evaluate the glucose disposal rate (GDR) in a study group consisting of 315 patients aged 7.6 to 19.7 years. The group was randomly divided into a training and independent testing set for model performance assessment. GDR was estimated on the basis of simple clinical variables using 2 non-linear methods: artificial neural networks (ANN) and multivariate adaptive regression splines (MARSplines). The results were compared against the most frequently used predictive model, based on waist circumference, triglyceride (TG), and HbA1c levels.

RESULTS

The reference model showed moderate performance ( R = 0.26) with a median absolute percentage error of 49.1%, and with the worst fit observed in young (7-12 years) children ( R = 0.17). Predictions of the MARSplines model were significantly more accurate than those of the reference model (median error 3.6%, R = 0.44 P < .0001). The predictions of the ANN, however, showed significantly lower error than those of the reference model (P < .0001) and MARSplines (P < .0001) and better fit regardless of patient age. ANN-estimated GDRs were within a ±20% error range in 75% of cases with a median error of 0.6% and an R = 0.66. The predictive tool is available at http://link.konsta.com.pl/gdr.

CONCLUSIONS

The developed GDR estimation model reliant on ANN allows for an optimized prediction of GDR for research and clinical purposes.