Identifying people at risk of developing type 2 diabetes: A comparison of predictive analytics techniques and predictor variables.
Journal:
International journal of medical informatics
Published Date:
Aug 28, 2018
Abstract
BACKGROUND: The present study aims to identify the patients at risk of type 2 diabetes (T2D). There is a body of literature that uses machine learning classification algorithms to predict development of T2D among patients. The current study compares the performance of these classification algorithms to identify patients who are at risk of developing T2D in short, medium and long terms. In addition, the list of predictor variables important for prediction for T2D progression is provided.