## 3 Discussion
### Diabetes Prediction Based ML/DL Techniques
However, there are some limitations (see Table A5): (a) It is not robust, a small change in the data can have a significant effect on the final predictions [36]. (b) It does not perform well with large datasets due to the increase complexity of the tree. Therefore, the preparation and the analysis become time consuming [17]. Still, many researchers in diabetes prediction use it, the accuracy ranges between 73% and 88%. Furthermore, different machine learning techniques were used in diabetes prediction, and DL techniques were utilized as well. The frequency of using the DL techniques in the last six years as shown in Table A2 (see the Appendix A) was calculated and presented in Figure 2. The most common techniques are Convolutional Neural Network (CNN), Multilayer Perceptron (MLP), Recurrent Neural Network (RNN), and Long-Term Short Memory (LTSM). Table A5 summarizes the advantages and disadvantages of these techniques. CNN is a special type of MLP, initially used in image processing. The underlying architecture of the CNN consists of three layers, a convolutional, a pooling, and a fully connected layer. The latter uses (ReLU) as the activation function [8]. CNN works well with a grid topology data type. The most significant advantage of CNN is that it performs automatic feature extraction. Also, it can show the correlation between the elements for the provided inputs [46]. CNN requires less time in classification and result in a high accuracy especially in image processing. However, the computational cost is high and it can't perform well without a large amount of training data [56]. It has been used in Diabetes prediction with the accuracy between 78%-96%. MLP is a supervised classifier, involving several layers and simple interconnected neurons that create a nonlinear mapping between the input and output vectors. During the training step, the weights are adjusted to reach the desired output. The difference between the target and the actual outputs is defined as an error signal. MLP can be applied to different data types such as texts and image. It is suitable for regression and prediction. MLP has many applications such as forecasting the level of ozone [57]. However, due to its fully interconnected neurons, the total number of parameters might  
Figure 2: Frequencies of Deep Learning Techniques. be very large which increases the processing time. Consequently, it can be inadequate for modern advanced computer vision field. Table 14 summarizes the advantages and disadvantages of this technique. MLP has been used in Diabetes prediction twice with an accuracy of 72.5% and 88.41% respectively. The second use was combined with General Regression Neural Network and Radial Basis Function. RNN is a powerful and robust type of ANN. It is different from the CNN as it takes sequential data inputs (natural language processing) instead of fixed-sized vectors (image). RNN processes the sequences of the inputs one at a time while saving the information about past neurons in the hidden neurons. Therefore, the outputs of different neurons at different time steps are inputs to the neurons of the next time step. RNN can perform flawlessly with complex problems such as Apple's Siri, predicting the next word in the text, or translation of the words that form a sentence [56]. This is due to its memorization ability (see Table 14). Therefore, it can be more accurate in the prediction problems [45]. Its main limitation is the vanishing gradients problem. It happens when the data get corrupted as it is multiplied by small numbers that are less than zero. Therefore, it is difficult to calculate the weights and biases which drop the accuracy. RNN was used twice in the last six years in Diabetes prediction. The highest accuracy obtained was 81%. LSTM is an improvement of RNN. It encompasses of multiple memory blocks that can overcome the vanishing gradient problem. Memory blocks are defined as complex processing units that consists of one or more memory cells to deal with long term dependencies.