## 1 Introduction  
Non-communicable diseases (NCDs) are estimated to comprise 72% of all deaths in Oman. Type 2 diabetes mellitus is the fourth most common reason for death, according to the World Health Organization and World Health Matrix [1]. An early diagnosis of T2 DM can prevent further medical complications such as heart disease, kidney failure, retinopathy, depression, and hypertension [2]. The Institute for Health Metrics and Evaluation (IHME) in Washington presented data on the worldwide burden of 369 diseases and injuries in 204 nations between 1990 and 2019 in 2020. Non-communicable and injury YLDs (years lived with disability) accounted for more than half of disease burdens in 11 countries in 2019. From 24% in 1990, Oman's T2 DM cases climbed to 49% [3]. By 2025, Oman's T2 DM rate will climb 174% from 10.4% to 21.1% in those over 20 [4].  
Numerous bioinformatics researchers have sought to forecast T2 DM and to develop tools and systems that will aid in forecasting. The accuracy rates achieved by various techniques ranged from 77.86% to 95.7%. They either developed prediction models utilising a variety of machine learning algorithms, such as association or classification algorithms [5; 6; 7; 8; 9; 10; 11], deep learning [12], data mining [13], or a combination model [14]. To improve accuracy, researchers have attempted to build models with classifiers that have not been used or that combine different classifiers [8; 15]. Many of the studies in the field of diabetes prediction have used the publicly available Pima Indian Diabetes dataset [16]. This paper presents a methodology to increase the prediction accuracy of T2 DM by using our dataset collected in Oman. The diagnosis system shown in this paper analyses two different diabetes datasets: the PID dataset and the Oman dataset, in which eight and eleven clinical features were applied, respectively. The Oman dataset was raw data that the authors gathered manually from Oman's primary and secondary care systems. It was collected after ethics approval was obtained from the Ministry of Health Research Centre in Oman. It was used for the first time in this research. It follows the ministry of health's guidance in diagnosing T2 DM. After pre-processing, the valuable features of both datasets were selected and then classified using seven classification techniques: K-nearest neighbours (K-NN), support vector machine (SVM), naive Bayes (NB), decision tree, random forest, discriminant analysis classifier (DAC), and artificial neural network.  
The proposed models aim to increase the classification accuracy of T2 DM diagnoses for the Oman population. The data collection process is illustrated in Figure 1. Related studies were addressed, and dataset creation and cleaning were carried out. The data pre-processing was carried out, and variable selections were decided. The dataset was then split into training and testing sets. The seven algorithms were implemented using the MATLAB platform; the dataset was trained and validated. Accuracy, sensitivity, specificity, and precision results were obtained and explained using a confusion matrix. The results were compared with similar state-of-the-art approaches from other researchers who have used the PID dataset.