# Prediction of Diabetes Using Statistical and Machine Learning Modelling Techniques
## 2 Literature Review
The authors used two class variables to find whether the patient had a risk of diabetes (positive) or not (negative). Three classification models were trained, namely Multilayer Perceptron (MLP), radial basis function network (RBF), and random forest (RF), mainly to obtain the best classifier model for predicting diabetes. Their findings showed that the RBF model outperformed other models, with an accuracy of 98.80%. [PERSON] et al. [18] developed a variety of machine learning models for the purpose of predicting the presence of diabetes in females using the PIDD dataset. They addressed the problem of missing values using the mean substitution technique, and all attributes were rescaled using a standardisation method. The constructed models are linear discriminant analysis (LDA), LR, SVM (linear and polynomial), and random forest (RF). Based on the results of their study, the highest accuracy score was achieved by the RF model, with 82%. Further to the studies that predicted or diagnosed diabetes, some existing studies have addressed the use of machine learning techniques to construct predictive models for diabetes complications. [PERSON] et al. [19] developed different classification models including LR, NB, SVMs, and random forest to predict the onset of retinopathy, neuropathy, and nephropathy in T2 DM patients. The authors used different time scenarios for making predictions, namely 3, 5, and 7 years from the first visit to the hospital for diabetes treatment. The dataset used to train the proposed models was collected by Istituto Clinico Scientifico Maugeri (ICSM), Hospital of Pavia, Italy, for longer than 10 years. These data involve a total number of 943 records including the features of gender, age, BMI, time from diagnosis, hypertension, glycated haemoglobin (HbA1c), and smoking habit. The problem of unbalanced and missing data was managed by applying the miss forest approach, while the problem of unbalanced class was overcome by oversampling the minority class. The obtained results of this study show that the highest accuracy score was achieved by LR with 77.7%. Another example is the model developed by [PERSON] et al. [20] to predict some complications related to diabetes, particularly hyperlipidemiaemia, coronary heart disease, kidney disease, and eye disease. A dataset of 455 records was used in this study. Selection and cleaning process were carried out on the dataset which reduced the number of recordsused to build the model. The number of features and the final number of records which were used to train the model were not mentioned by the authors. An iterative decision tree (ID3) algorithm was chosen to construct the model. For evaluating the performance of the proposed model, a 10-fold cross validation method was used, giving an accuracy of 92.35%. It should be noted that the high accuracy score obtained by this study is not sufficient to evaluate the performance of the model, especially when training unbalanced data. The main reason for this is that when the model trains the data, a minority class can be ignored, and all the predictions are classified as the majority class and the good accuracy scores are still achieved. Although machine learning methods have been utilised in other aspects of diabetes research, most of them are based on diagnosing or detecting the disease, and little research attention has explored the adoption of machine learning methods to study the trends in the prevalence of diabetes and forecast its future in specific populations such as in the KSA. Thus, this paper attempts to apply various machine learning methods for studying diabetes prevalence rates and the predicted trends of the disease according to the related behavioural risk factors in the KSA.