*Background*

Numerous authors work to develop appropriate disease prediction algorithms. For instance, Lélis et al. applied seven classification techniques in a Brazilian investigation to make a diagnosis of meningococcal meningitis and verified that the model is a ffordable and accurate [9]. Susanne et al. proposed a mathematical model to forecast the prevalence of diabetes by using attributes of sex, age, risk factor status, and T2DM (type 2 diabetes mellitus) status and found T2DM prevalence is projected to increase by 43%, and the incidence is projected to increase 147% by 2050 in Qatar [10]. Choi et al. applied support vector machine (SVM) and artificial neural network (ANN) to screen the pre-diabetes of 9251 individuals and performed a systematic assessment of the models using external and internal cross-validation and concluded that the results of the SVM method are better than the ANN [11]. Amir et al. proposed a time series prediction model for the diagnosis of diabetes patients [12]. In addition, Olivera et al. utilized machine learning algorithms from ELSA-Brazil and identified individuals with the highest risk of undiagnosed diabetes from readily available clinical data [13]. Sohail et al. performed the classification results on Weka by machine learning by utilizing the dataset of di fferent diseases and concluded the accuracy ratio of the decision tree (86%), the Bayesian network (90%), the naïve Bayesian (76%), the fuzzy cognitive map (94%), and K-nearest neighbor (KNN) (94%) [14]. Parampreet et al. applied a cloud-based framework with the help of sensor devices to initially screen patients for the prediction of diabetes [15]. Further, Hassan et al. proposed a unified machine-learning framework for diabetes predications in big data [16]. There is considerable interest in determining how di fferent classification techniques from machine learning can be utilized as disease prediction tools [17–21]. These tools have been used to diagnose diabetes [22], glaucoma [23], meningitis [24], coronary artery disease [25], asthma [26], cancer [27], hypertension [28], heart arrhythmia [29], tuberculosis [30], and other diseases [31,32].

#### **2. Material and Methods**
