Predicting insulin resistance in children using a machine-learning-based clinical decision support system

Citation

Hall AJ, Hussain A & Shaikh MG (2016) Predicting insulin resistance in children using a machine-learning-based clinical decision support system. In: Liu C, Hussain A, Luo B, Tan K, Zeng Y & Zhang Z (eds.) Advances in Brain Inspired Cognitive Systems. BICS 2016. Lecture Notes in Computer Science, 10023. BICS 2016: 8th International Conference on Brain-Inspired Cognitive Systems, Beijing, China, 28.11.2016-30.11.2016. Cham, Switzerland: Springer, pp. 274-283. https://doi.org/10.1007/978-3-319-49685-6_25

Abstract
This study proposes a new diagnostic approach based on application of machine learning techniques to anthropometric patient features in order to create a predictive model capable of diagnosing insulin resistance (HOMA-IR).  As part of the study, a dataset was built using existing paediatric patient data containing subjects with and without insulin resistance. A novel machine learning model was then developed to predict the presence of insulin resistance based on dependent biometric variables with an optimal level of accuracy. This model is made publicly available through the implementation of a clinical decision support system (CDSS) prototype. The model classifies insulin resistant individuals with 81% accuracy and 75% of individuals without insulin resistance. This gives an overall accuracy of 78%. The user testing feedback for the CDSS is largely positive.  Best practices were followed for building the model in accordance to those set out in previous studies. The biometric profile of insulin resistance represented in the model is likely to become better fitted to that of insulin resistance in the general population as more data are aggregated from sources. The infrastructure of the CDSS has also been built so that cross platform integration will be possible in future work.  The current methods used by clinicians to identify insulin resistance in children are limited by invasive and clinically expensive blood testing. The benefits of this model would be to reduce the cost of clinical diagnosis and as a result, could also be used as a screening tool in the general childhood population.

Keywords
Machine learning; Ensemble learning; Clinical Decision Support System; Insulin resistance; Diabetes; Paediatrics