## 2 Related Work  
In India, diabetes is an inescapable problem as over 70% of the grown-up populace suffers from diabetes. Different scientists attempted to detect and predict diabetes by applying various ML and data mining methodologies, and some applied deep learning and fuzzy logic [15]. Data mining approaches have supplanted old procedures because they are more accurate, precise, and predictive in their predictions. Furthermore, machine learning is an artificial intelligence system that learns correlations between nodes without the need for previous training [16]. The significant capacity of machine learning approaches to drive the prediction model without extensive training is connected to the mechanism that underlies these techniques. Methods such as data mining and machine learning assist in detecting information that is otherwise difficult to identify when utilizing a cutting-edge technique [17]. Some of the past related research work focuses on the detection and prediction of diabetes using PIDD [14; 18; 19; 20; 21; 22]. Numerous ML methods, such as decision trees, RF, SVM, and Naive Bayes, have recently shown promising outcomes in various types of medical research. [PERSON] et al. [23] suggested a way to identify diabetes in female Pima Indian populations by using a neural network and support vector machine (SVM) models. [PERSON] et al. [24] diagnosed diabetes using the prognosis of fuzzy c-means clustering and SVM. [PERSON] et al. [1] used PIDD and chose the best attributesthat are essential for classification and excluded the remainder, such as plasma, glucose, postprandial, pregnancy, serum creatinine, and HBAIC attributes, from the dataset and applied different ML algorithms such as SVM [25], Naive Bayes, and RF. However, SVM outperformed the simplest with an accuracy of 77.37%. [PERSON] et al. [26] suggested a region-based SVM method for the diagnosis of diabetes on the Pima Indian medical dataset. Similar to [PERSON], [PERSON] et al. [15] suggested SVM for the detection of Type 2 diabetes. [PERSON] et al. [27] applied a decision tree approach to identify type 2 diabetes. The researchers [PERSON] et al. [18] and [PERSON] et al. [28] developed a web-based strategy to forecast diabetes using machine learning techniques on the PIMA Indian dataset. On the other hand, no single study has examined all the well-known supervised learning methods in a comprehensive manner. [PERSON] et al. [20] used an ANN approach to predict diabetes using the PIMA Indian dataset. [PERSON] et al. [29] developed a multilayer perceptron that was used to predict diabetes. Using an autotuned multilayer perceptron, [PERSON] et al. [30] suggested an expert system to predict diabetes. [PERSON] et al. [31] proposed a DNN for the classification of Type 2 diabetes using stacked encoders for feature engineering, a softmax function for classification, and a backpropagation method for fine-tuning the network. Training of the model was performed with PIDD along with 786 patient records and eight features and achieved an accuracy of 86.26%. [PERSON] et al. [32] used the decision tree technique to predict type-2 diabetes using the PID dataset [33]. A comparison of performance using an SVM classifier showed that the decision tree successfully predicted type-2 diabetes. In summary, the above-discussed techniques have some pros and cons, which are as follows: For example, ML algorithms such as RF, decision trees, and SVM are helpful if we use them for classification problems, except for regression, where they may not be suitable for predicting training data beyond the range. Similarly, within the decision tree, if there is a little change in data, it may affect the entire structure of the model [31]. Furthermore, SVM faces minor issues with noisy data [34]. Therefore, these ML algorithms are suitable for classification problems. However, ANN and CNN are good at making predictions because, in backpropagation, these methods obtain good results when they use gradients to update the weights.