## 3 Discussion
### Diabetes Prediction Based ML/DL Techniques  
Based on the studies conducted to predict diabetes in the last six years (shown in Table A1), the frequency of using each machine learning classifier was calculated. Artificial Neural Network (ANN), Support Vector Machine (SVM), Decision Tree, and Naive Bayes were the most used classifiers for diabetes prediction as displayed in Figure 1. Table A5 summarizes the advantages and disadvantages of all these algorithms. Artificial Neural Networks (ANN) are inspired by the human neurological system mechanism [49]. They can learn by experience and extract the essential attributes from inputs that contain unnecessary data, and deal with vague cases [50; 51]. The basic architecture of an ANN consists of three layers; the input, the output, and the hidden layers. The hidden layer includes neurons that process the operations on data to improve the ability to learn. The number of hidden layers in the neural network affects its performance, too many hidden layers will result in an overfitting problem [52]. Multilayer Perceptron (MLP), Bayesian Neural Network (BNN), and Probabilistic Neural Network (PNN) are types of ANN models. Table A5 summarizes the advantages and disadvantages of this technique. ANN has been applied in many fields such as prediction and forecasting, classification, data correlation and association, robotics and data filtering [53; 54; 55]. Moreover, it has been used in predicting diabetes, and it attained an accuracy value ranging between 60% and 95%. SVM is a statistical supervised ML classifier used in binary classification tasks. It is defined by a separated hyperplane between the two output classes [19]. SVM uses a set of mathematical functions called kernels to transform the input into the required form. Different types of kernel functions exist such as polynomial, linear, and nonlinear. SVM works well with unstructured and semi-structured datasets such as images and text by using the appropriate kernel function [18]. Although it is a powerful technique, it has some limitations (see Table A5). It is hard sometimes to select the right kernel function. Also, when it is used with large datasets, it requires long training time. Even though the weights of the variables are difficult to interpret in the final model, it can attain accurate and robust results [34]. SVM is a frequently used technique in medical applications. It was also applied in diabetes prediction in many research studies. The obtained range was 65%-96%. Naive Bayes is a statistical classification technique based on Bayes' theorem. It is about calculating the probability of the output based on the prior knowledge of the relationship between the attributes [19]. It operates with the concept that all the features are independent of each other. It works well with the data that has missing values or unbalancing issues, with categorical and continuous values. It is easy to implement and require a small amount of data to set its parameters [31]. All these properties made it a powerful classifier to be used in the medical diagnosis's problems. Practically, the variables might have dependencies. The problem with this classifier is that the conditional independence might cause a drop in the accuracy. Table A5 summarizes the advantages and disadvantages of this technique. The accuracy obtained in predicting diabetes ranged between 76% and 96%. Figure 1: Frequencies of Machine Learning Classifiers. Decision Tree is a supervised ML technique to solve prediction, classification, and feature selection problems. It aims to predict the target class based on the rules learned from the given dataset [19]. The decision tree is an excellent option for researchers due to its advantages. It is easy to understand and implement especially when using a graphical representation of the concept. It works well with numerical and categorical data [4]. Since it works with different data types, it does not require much data pre-processing work.