## 2 Related Works
### Related Works Using Deep Learning  
Researchers have started to realize the capabilities of the DL techniques in processing large datasets. Therefore, diabetes prediction has also been performed using DL techniques. Seven studies were published during the six last years, as shown in Table 11 (see the Appendix A). Also, [PERSON] et al. [7] used a Deep Neural Network (DNN). The architecture of the DNN composed of Multilayer Perceptron (MLP), General Regression Neural Network (GRNN), and Radial Basis Function (RBF). The evaluation of the approach was based on the Pima Indian dataset. The authors didn't pre-process the dataset intentionally as DNN can filter the data and acquire the biases. The dataset is split into 192 samples for the testing set and the rest for the training. The accuracy rate reported by the authors was of 88.41%. Another study by [PERSON] et al. [8] used two DL techniques to improve the accuracy of diabetes prediction. A private dataset called Electrocardiograms was used to assess the performance of the CNN and CNN-LSTM. It consisted of 142,000 samples and eight attributes (see Table 10). Five-fold cross validation was used to split the dataset into training and testing sets. The authors did not pre-process data nor apply feature selection method because of the self-learning of DNN. The generated accuracy rates for the models were 90.9% and 95.1%, respectively. [PERSON] et al. [42] used logistic regression as a baseline to multilayer perceptron neural network and conventional neural network (CNN). The aim was to detect diabetic patients based on a continuous glucose monitoring (CGM) signal dataset. The dataset is composed of nine patients and each patient had 10,800 days of CGM data, resulting in a total of 97,200 simulated CGM days. The attributes used in this study were not discussed. The dataset was split into training, validation, and testing sets based on leave-one-patient-out cross-validation technique. In fact, the authors selected six patients for training and validation, and three patients for testing. The CNN achieved the highest accuracy of 77.5%. Moreover, [PERSON] et al. [43] proposed a framework of unsupervised Deep Neural Network called Deep Patient. The framework used a patients' electronic health records database composed of 704,857 patients. The authors didn't specify the features used in this dataset, they mentioned that the dataset can be used to predict different diseases. In the validation process, the authors split the data into 5000 (patients) for the validation, 76,217 (patients) for the testing and the rest for the training. The accuracy was measured based on the Area Under Curve (AUC) which achieved 0.91. The authors recommended to pre-process the dataset to well enhance the prediction performance. They suggested using PCA to extract relevant attributes before performing the DL. [PERSON] et al. [44] applied three different DL techniques on a manually collected dataset from a regional Australian hospital. The dataset is composed of 12,000 samples (patients) containing 55.5% males. Some pre-processing techniques (not mentioned in their article) have been applied to clean and reduce the samples to 7191 patients. For validation, the dataset was split to 2/3 for the training set, 1/6 for the validation and 1/6 for the testing. The methods were Long Short-Term Memory (LTSM), Markov, and Plain RNN. The precision value was used to compare the performance of the techniques. The best precision value of 59.6% was achieved by using the LTSM. Furthermore, [PERSON] et al. [45] used the Recurrent Neural Network (RNN) to predict the two types of diabetes. The authors utilized the Pima Indian dataset with 768 samples and eight attributes. The attributes are ordered according to their highest importance as indicated in their study \"Glucose, BMI, Age, Pregnancies, Diabetes Pedigree Function, Blood Pressure, Skin Thickness and Insulin\". To validate the study, the dataset was split into 80% for the training and 20% for the testing.