## 2 Recent Contributions to Classify Diabetes  
Researchers in the field of data science have recently made efforts to discover, detect, and diagnose diabetes in its early stage. The use of classification and regression algorithms can handle and aggregate large-scale patients simultaneously to help track disease. Although tracking diabetes is helpful, efforts to recommend a suitable treatment protocol are being made using machine learning and computer vision methodologies [19; 20]. The PIDD was considered one of the most well-known datasets used in classification and prediction issues in diabetes diseases. A comparative review of prediction approaches for diabetes was presented by [PERSON] and [PERSON] [21]. Moreover, a systematic review of machine learning and artificial intelligence approaches utilized to predict and self-manage diabetes based on the PIDD was presented by [PERSON] et al. [5]. A framework for a semantic retrieval algorithm to diagnose diabetes based on fuzzy ontology orientation was presented by [PERSON] et al. [22]. The results achieved based on fuzzy ontology were 97.67% for 54 (fuzzy) object properties, 138 (fuzzy) datatype properties, 105 fuzzy data types, and 2640 instances. A prediction of diabetes using classification algorithms such as decision tree, support vector machine (SVM), and NB are presented by [PERSON] et al. [23]. In this work, diabetes was predicted at an early stage. The accuracy achieved was 76.3 on the PIDD. A hybrid model based on a GA and a backpropagation network (BPN) was proposed by [PERSON] et al. [24], in which the weights of the BPN were optimized in the initialization based on the GA. The obtained accuracy value was between 77.07% and 84.71% when usingcorrelation-based feature selection GA and BPN. A sequential minimal optimization (SMO) based on quadratic programming was used on the PIDD dataset by [PERSON] and [PERSON] [25] with an average accuracy reaching 77.35%. The clinical PIDD dataset was further utilized to predict diabetes in women of Pima Indian heritage using supervised learning approaches investigated by [PERSON] et al. [26]. They compared and evaluated the results in terms of accuracy, precision, recall, F1 score, and AUC for the output of the classification tree (CT)-, SVM-, KNN-, NB-, random forest (RF), neural network (NN)-, AdaBoost (AB)- and logistic regression (LR)-supervised classifiers. [PERSON] and [PERSON] [27] presented an RF algorithm for the Pima dataset using 768 instances and 100 trees for the applied forest, and the results obtained were between 70% and 80% accuracy. The AB can be utilized efficiently to classify the enrolled data based on a genetic algorithm [28]. [PERSON] et al. [29] presented an end-to-end healthcare monitoring framework for managing diabetes and predicting risk cases on the Pima dataset. They achieved 83.20%, 87.20%, and 79% accuracy, sensitivity, and specificity, respectively. An algorithm based on backward elimination and SVM was presented by [PERSON] et al. [30] to classify the PIDD. They utilized 268 diabetic patients and 500 normal patients with 8 well-known attributes, and they achieved an average accuracy of 85.71%. [PERSON] et al. [31] presented an LR method to predict the probability of diabetes based on the PIDD dataset, and they achieved 77.06% accuracy based on Python. An enhanced early detection method for diabetes based on type 2 diabetes mellitus (T2 DM) using multiple classifier approaches was presented by [PERSON] et al. [32]. Multiple weighted factors based on a dynamic weighted voting scheme were used to adapt the prediction decision of this method to employ all voters in an equal manner and to combine these decisions. [PERSON] et al. [33] proposed diabetic prediction approaches that include data augmentation using variational autoencoder (VAE), and the features are augmented using sparse autoencoder (SAE).