# Prediction of Diabetes Using Statistical and Machine Learning Modelling Techniques
## 2 Literature Review  
In the last few decades, several studies have predicted the incidence of diabetes and its global prevalence for different countries around the world, using diverse data and methods of analysis. [PERSON] et al. [10] estimated diabetes prevalence by the number of diabetics aged 20 years and over for every country in the world in three time points: 1995, 2000, and 2025. Other variables were calculated, such as the gender proportion, urban-rural proportion, and age groups of the population who suffer diabetes. The data used in this study were obtained from the World Health Organisation's (WHO) global database, which was collected from 75 societies representing 32 countries. To estimate the number of diabetes cases in every country in the world, data gathered from the WHO were linked to demographic estimates and projections released by the United Nations. The study assumed that, besides ethnicity, other factors contribute to diabetes trends, such as population size, sex, age structure, and urbanisation level. All data sources were analysed using logistic regression modelling. The global prevalence of diabetes in 1995 was estimated to be 4.0%, predicted to increase to 5.4% by the year 2025. This was higher in developed than developing countries. [PERSON] et al. [11] developed an updated report in 2004, adding new data and various techniques to estimate age-specific diabetes prevalence. This study estimated the prevalence of diabetes, and the number of diabetics in all age groups, for the years 2000 and 2030. For this study, diabetes prevalence data according to age and sex were collected from a restricted range of countries and extrapolated to all 191 states represented by the WHO. For people aged 20 and over, the data were obtained using population-based studies, using WHO criteria for diagnosing diabetes. In order to generate smooth, age-specific estimates, DisMod II version 1.01 software was used, which is a mathematical model for analysing estimations of disease with regard to occurrences, prevalence, and mortality rates. It was estimated that the global prevalence of diabetes for all age groups was 2.8% in 2000, projected to rise to 4.4% in 2030; a total of 171 million diabetic people in 2000 was predicted to increase to 366 million by 2030. A study by [PERSON] et al. [12] aimed to predict the number of diabetes cases globally for 2010 and 2030. Studies were collected from the 91 countries in which they were published between January 1989 and March 2009. A total of 133 studies that used a population-based method to evaluate the prevalence of diabetes were selected, applying the diagnostic measures of the WHO or the American Diabetes Association (ADA). Age- and sex-specific diabetes prevalence in people aged 20-79 was calculated using logistic regression modelling. These calculations were applied to the estimates of national populations to estimate the number of diabetic people for all 216 countries for 2010 and 2030. It was estimated that the global prevalence of diabetes within the 20-79 age group was 285 million adults in 2010, projected to rise to 439 million by 2030. The recent literature has produced a significant amount of research on diabetes using several techniques. These techniques have been used for various purposes, such as diagnosing or detecting diabetes at an early stage, and for modelling the disease's progression and complications. A study by [PERSON] et al. [13] used three different types of regression analysis methods, linear, polynomials, and exponential, to develop models for predicting the number of diabetic patients in Kazakhstan in 2019. Their study aimed to develop a model that can predict the increase in the number of diabetics using regression analysis methods, and to identify the most effective experimental method for predicting diabetes. The data of diabetic patients were obtained from a public foundation, the Kazakh Society for the Study of Diabetes.