## 2 Literature Review  
Past studies have focused on diagnosing diabetes using the PID dataset, and few have used private clinical datasets [17; 18; 19; 20; 21; 22; 23; 24; 25; 26; 27]. Many AI techniques have been used to develop a predictive model. Various researchers have used ML algorithms to predict diabetes to achieve the best and most accurate results [28; 29]. Figure 1: Dataset creation. However, none of the research studies reported below described the data pre-processing step implemented on the dataset nor explained how they dealt with the noise found in the dataset. They presented the results and accuracy obtained by the J48 decision tree classifier as 73.82%. K-NN (k = 1) classifiers and RF disclosed the highest accuracy percentage of 100 after pre-processing the data. [PERSON] and [PERSON] [30] proposed an application for diabetes prediction employing random forest, decision tree, and naive Bayes. They used the PID dataset after its pre-processing. They described the data-gathering technique utilised to pick the pertinent features. They barely used eight of the thirteen potential primary qualities. Additionally, they allocated 70% of the dataset for training and 30% for testing. The findings showed that the random forest method had the best rate of accuracy, 94%. [PERSON] and [PERSON] [31] used the same dataset to determine which classifier from the decision tree, naive Bayes, and SVM selection could achieve the highest accuracy in detecting T2 DM. The partition of the dataset was made by employing 10-fold cross-validation. Accuracy, precision, recall, and the F-measure were used to describe the performance. The naive Bayes obtained the highest accuracy, reaching 76.30%. [PERSON] et al. [32] used six different classifiers: J48, multilayer perceptron, Hoeffding tree, random forest, JRip, and Bayes net. However, they employed the greedy stepwise and best first algorithms to determine the discriminatory attributes that helped to increase the classification prediction. These attributes were diabetic pedigree function, plasma glucose, and age. Dataset validation was applied using 10-fold cross-validation. The classifiers were compared based on the F-measure, precision, and recall value. The findings of the Hoeffding tree algorithm indicated that the accuracy value was 0.75, the recall value was 0.76, and the F-measure was 0.75. [PERSON] et al. [19] linked a diabetes risk calculation. Four well-known machine learning classification techniques--decision tree, artificial neural systems, logistic regression, and naive Bayes--were examined to meet the goal. Bagging and boosting processes are used to strengthen structured models. The random forest calculation produced the best results of all methods tested. [PERSON] et al. [27] examined the diabetes diagnostic dataset. Luzhou, China's hospital physical examination data were used. The dataset included 220,680 patients and 14 characteristics. A total of 151,598 (69%) were diabetic, whereas 69,082 (31%) were controlled. They used PCA and minimal redundancy maximum relevance (mRMR) to minimise dimensionality and K5 cross-validation methodology to analyse the data. They classified diabetic patients using three classifiers: DT, NN, and RF. The RF-based classifier had the greatest classification accuracy of 80.84%. [PERSON] et al. [33] developed a machine learning (ML)-based method to detect diabetes patients using logistic regression (LR) to identify risk variables based on the odds ratio (OR) and \(p\)-value. They forecast diabetes patients using four classifiers: Adaboost (AB), decision tree (DT), random forest (RF), and naive Bayes (NB). Three partition protocols--K2, K5, and K10--have also been used in 20 trials. Accuracy (ACC) and area under the curve (AUC) assessed these classifiers. They used diabetes data from the 2009-2012 National Health and Nutrition Examination Survey. LR model shows that 7 of 14 risk factors for diabetes include age, BMI, education, diastolic BP, systolic BP, total cholesterol, and direct cholesterol.