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

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.