Harnessing machine learning models for non-invasive pre-diabetes screening in children and adolescents. (November 2022) - Kushwaha, Savitesh; Srivastava, Rachana; Jain, Rachita; Sagar, Vivek; Aggarwal, Arun Kumar; Bhadada, Sanjay Kumar; Khanna, Poonam
Highlights: We have used a national level data set for non-invasive pre-diabetes screening. The eight non-invasive features for pre-diabetes prediction in children and adolescents were used. We have trained six hypertuned machine learning models for classification between normal and pre-diabetes. We have deployed the most efficient machine learning model for real-time pre-diabetes screening. Abstract: Background and objectives: Pre-diabetes has been identified as an intermediate diagnosis and a sign of a relatively high chance of developing diabetes in the future. Diabetes has become one of the most frequent chronic disorders in children and adolescents around the world; therefore, predicting the onset of pre-diabetes allows a person at risk to make efforts to avoid or restrict disease progression. This research aims to create and implement a cross-validated machine learning model that can predict pre-diabetes using non-invasive methods. Methods: We have analysed the national representative dataset of children and adolescents (5–19 years) to develop a machine learning model for non-invasive pre-diabetes screening. Based on HbA1c levels the data ( n = 26, 567) was segregated into normal ( n = 23, 777) and pre-diabetes ( n = 2790). We have considered eight features, six hyper-tuned machine learning models and different metrics for model evaluation. The final model was selected based on the area under the receiver operator curve (AUC), Cohen's kappa and cross-validation score. The selected model was integrated into the screening tool for automated pre-diabetes prediction. Results: The XG boost classifier was the best model, including all eight features. The 10-fold cross-validation score was highest for the XG boost model (90.13%) and least for the support vector machine (61.17%). The AUC was highest for RF (0.970), followed by GB (0.968), XGB (0.959), ETC (0.918), DT (0.908), and SVM (0.574) models. The XGB model was used to develop the screening tool. Conclusion: We have developed and deployed a machine learning model for automated real-time pre-diabetes screening. The screening tool can be used over computers and can be transformed into software for easy usage. The detection of pre-diabetes in the pediatric age may help avoid its enhancement. Machine learning can also show great competence in determining important features in pre-diabetes.