Predicting diabetic ketoacidosis in pediatric patients using machine learning

Abstract

Background Machine learning is a powerful tool to define relationships between large data variables through computing algorithms. In medicine, machine learning can find the association between a given disease and disease-related complications such as the relationship between Diabetes and development of diabetic ketoacidosis (DKA).  The aim of this study is to develop and evaluate a predicting model for diabetic ketoacidosis among pediatric cases to define the leading factors that can predict diabetic ketoacidosis.  Methods We evaluated the medical records of 3737 pediatric patients between the ages of 0 and 18 years who attended diabetic clinics and were diagnosed with diabetes. After the initial data preprocessing, we used Orange, an open source software, for data visualization, and machine learning for data analysis. The study used six prediction models: Decision Tree, Random Forest, kNN, Gradient Boosting, CN2 rule inducer and AdaBoost. Data imbalance was managed using oversampling technique. Variables analyzed included age, sex, hemoglobin A1C level, visits to the diabetic education clinic, and number of appointments to diabetic clinic. Models were evaluated based on the Area under the Curve (AUC), accuracy, precision, recall and F1-score using the stratified 5-fold cross validation technique.  Results The results show that the Random Forest is the highest performance classifier (AUC=0.98; F1 score=0.92; and recall=0.93). Furthermore, HbA1c was the most contributing factor to the prediction model. Conclusion This study shows the importance and effectiveness of machine learning modeling to predict the association between diabetes and the development of DKA. Flagging those patients who are at a higher risk of developing DKA provides a better point of care for these patients.