Type 2 Diabetes with Artificial Intelligence Machine Learning: Methods and Evaluation

Abstract

Abstract Diabetes, one of the top 10 causes of death worldwide, is associated with the interaction between lifestyle, psychosocial, medical conditions, demographic, and genetic risk factors. Predicting type 2 diabetes is important for providing prognosis or diagnosis support to allied health professionals, and aiding in the development of an efficient and effective prevention plan. Several works proposed machine-learning algorithms to predict type 2 diabetes. However, each work uses different datasets and evaluation metrics for algorithms’ evaluation, making it difficult to compare among them. In this paper, we provide a taxonomy of diabetes risk factors and evaluate 35 different machine learning algorithms (with and without features selection) for diabetes type 2 prediction using a unified setup, to achieve an objective comparison. We use 3 real-life diabetes datasets and 9 feature selection algorithms for the evaluation. We compare the accuracy, F-measure, and execution time for model building and validation of the algorithms under study on diabetic and non-diabetic individuals. The performance analysis of the models is elaborated in the article.