# Prediction of Diabetes Using Statistical and Machine Learning Modelling Techniques
## 2 Literature Review
Data on patients with diabetes from 2004 to 2018 were used to build predictive models by finding patterns over the last 15 years, and then these models could accurately predict the prevalence of diabetes in Kazakhstan. The proposed models were implemented in scikit-learn library for the Python programming language and Microsoft Excel software. The results showed that the number of diabetes patients will increase, and that there was a strong correlation of population growth with the increase in the number of diabetic patients. Their findings indicated that all the three types of regression had high coefficients of determination R\({}^{2}\) which was always above 0.90; however, the polynomial regression model achieved the highest R\({}^{2}\) value, which means it was the best suited for predicting the number of diabetes patients. Another study performed by [PERSON] et al. [14] developed the random forest (RF) and extreme gradient boosting (XGB) regression models and an ensemble model based on linear combination of the RF and XGB models for HbA1c prediction. These models were used to predict the average amount of glucose accumulated in the blood over the last 2-3 months using past continuous glucose monitoring (CGM) data. Predicting the levels of HbA1c in advance helps to determine direct relationships with diabetes and to avoid the future risk of complications. In this study, the dataset was collected from the Diabetes Research in Children Network (DirecNet) trials on a total of 170 patients having T1 DM. Furthermore, various methods for feature extraction and selection were used to prepare the dataset. The findings obtained by this study show that the best performance was achieved by the constructed model which involved two ensemble methods, RF and extreme gradient boosting (XGB), with a low mean absolute error (MAE) of 3.39 mmol/mol and a high score of coefficients of determination R\({}^{2}\) of 0.81. [PERSON] et al. [15] aimed to evaluate the performance of classification algorithms on the prediction of diabetes. In this study, the PIMA Indian data repository was used, which included a total of 768 samples. These data were divided into training and testing sets, with 70% for training (538 samples) and 30% for testing (230 samples). This study examined the implementation of eight machine learning models, namely logistic regression (LR), (KNN), (SVM), Gradient Boost, decision tree, Multilayer Perceptron (MLP), random forest, and Gaussian Naive Bayes. The results showed that the highest accuracy was achieved by the logistic regression model, with 79.54% and RMSE of 0.4652; the lowest accuracy was given by the Multilayer Perceptron (MLP), with 64.07% and RMSE of 0.5994. The authors suggested improving the obtained results by using outlier detection before classification. A comparative study conducted by [PERSON] et al. [16] used different machine learning models, including SVM, C4.5 decision tree, Naive Bayes, and KNN, and used the evaluation metrics of accuracy, recall, and precision to compare the performance of the classification models on predicting diabetes. In their study, they collected diabetes data from the diagnostics of Medical Centre Chittagong (MCC), Bangladesh. The dataset includes 200 patients with various attributes such as age, sex, weight, blood pressure, and other risk factors. The results obtained from this study indicated that the best performance was achieved by the C4.5 decision tree model with an accuracy of 73%. In another study, [PERSON] et al. [17] proposed a classification model aimed at the early detection of diabetes using machine learning algorithms. This study was designed to use significant features and deliver results which are close to the clinical outcomes. The data used in this study were collected from patients using direct questionnaires from the Diabetes Hospital of Sylhet, Bangladesh. This dataset includes reports of diabetes-related symptoms of 520 instances with 16 attributes.