Feasibility Study of Constructing a Screening Tool for Adolescent Diabetes Detection Applying Machine Learning Methods

Abstract Prediabetes and diabetes are becoming alarmingly prevalent among adolescents over the past decade. However, an effective screening tool that can assess diabetes risks smoothly is still in its infancy. In order to contribute to such significant gaps, this research proposes a machine learning-based predictive model to detect adolescent diabetes. The model applies supervised machine learning along with a novel feature selection method on the National Health and Nutritional Examination Survey datasets after an exhaustive search selecting reliable and accurate data. The best model achieved an area under the curve (AUC) score of 71%. This research proves that a screening tool based on supervised machine learning models can assist the automated detection of youth diabetes. It also identifies some critical predictors to such detection with a combination of Lasso Regression, Random Forest Importance and Gradient Boosted Tree Importance feature selection methods. The most contributing features to Youth diabetes detection are physical characteristics (waist, leg length, gender etc.), dietary information (water, protein, sodium etc.) and demographics (race). These predictors can be further utilised in other areas of medical research, such as electronic medical history.