# Prediction of Diabetes Using Statistical and Machine Learning Modelling Techniques
## 1 Introduction  
Diabetes is a serious health problem that is growing significantly around the world because of several demographic and behavioural factors, including increasing population density, urbanisation, an aging population, the prevalence of obesity, and low physical activity. Diabetes Mellitus (DM) is a group of metabolic disorders characterised by chronic hyperglycemia due to deficiencies in insulin production, resistance to insulin, or both. This condition leads to abnormalities in the metabolism of carbohydrates, fats, and proteins, and, over time, it can result in complications affecting various organs, including the eyes, kidneys, nerves, heart, and blood vessels [1, 2]. There are three types of diabetes classified according to aetiology and clinical picture: type 1 diabetes, type 2 diabetes, and gestational diabetes. Patients with type 1 diabetes need insulin injections to survive, while type 2 diabetes, which represents most cases, is a defect in the secretion and function of insulin, meaning some diabetics of this type need insulin but most do not as they continue to produce insulin. Gestational diabetes is recognised or first starts during pregnancy, which is characterised by glucose intolerance of varying degrees of severity [3].  
Chronic diseases such as diabetes that are associated with lifestyle factors have become the most prevalent and the most significant threat to health. The increasing rate of diabetes and its associated complications has been reaching an alarming level worldwide. The prevalence rate of diabetes is higher in developed countries than in developing countries; however, during the past two decades, diabetes has been reported at higher levels in developing countries [4]. Official statistics published by the International Diabetes Federation (IDF) indicate that there were more than 460 million people with diabetes in 2019; this figure is expected to increase to 578 million in 2030, and 700 million in 2045. The IDF also reported that in the Kingdom of Saudi Arabia (KSA), the case of our study, there are currently an estimated 4 million diabetic patients [5].  
The increasing prevalence of diabetes has prompted researchers around the world to investigate methods for the prediction and early diagnosis of diabetes. A variety of published studies have predicted the incidence of diabetes and its global prevalence for different countries around the world, including the KSA, using diverse data and methods of analysis. Future estimates of the burden of diabetes are very important for health policy planning and resource allocation [6; 7]. Recently, machine learning algorithms have been widely used in public health for predicting or diagnosing epidemiological chronic diseases, including DM. There are many published diabetes studies that used different machine learning techniques, including Support Vector Machines (SVMs), Artificial Neural Network (ANN), K-Nearest Neighbour (KNN), fuzzy logic (FL), and decision tree [8; 9].  
This study contributes to developing different statistical and machine learning methods, namely Multiple Linear Regression (MLR), Adaptive Neuro-Fuzzy Interference System (ANFIS), Artificial Neural Network (ANN), Support Vector Regression (SVR) and Bayesian Linear Regression (BLR), for the purpose of describing the prevalence pattern of diabetes and obtaining predictions of the future level of the disease. The rest of the paper is organised as follows: Section 2 reviews the literature. Section 3 presents the proposed methodologies. Section 4 introduces the experimental methodology. Section 5 presents the results. Section 6 provides a discussion of the findings. Finally, Section 7 concludes this paper and identifies areas for future research.