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.
Highlights
Diabetes Mellitus, commonly referred to as diabetes, is a chronic disease that affects how the body turns food into energy [1]
We reveal the reasons behind the performance of these algorithms
If a diabetic patient having all the risk factors is clustered in the nondiabetes cluster, the patient will be removed by the k-means as an outlier
Summary
Diabetes Mellitus, commonly referred to as diabetes, is a chronic disease that affects how the body turns food into energy [1]. It is one of the top 10 causes of death worldwide with 4.2 million deaths in 2019 [2]. There are three main types of diabetes: type 1, type 2, and gestational diabetes [1]. Type 1 diabetes is thought to be caused by an autoimmune reaction where the body’s immune system affects the insulinproducing beta-cells. Type 2 diabetes is caused by inadequate production of insulin and the inability of the body cells to respond to insulin properly.
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