## 2 Related Works
### Related Works Using Machine Learning  
ML algorithms are very well-known in the medical field for predicting diseases. Many researchers have used ML techniques to predict diabetes in an effort to obtain the best and most accurate results [16]. [PERSON] and [PERSON] [4] used multiple classifiers SVM, J48, K-Nearest Neighbors (KNN), and Random Forest. The classification was performed on a dataset taken from the UCI repository (for more details see Table 14). The results of the classifiers were compared based on the values of the accuracy, sensitivity, and specificity. The classification was done in two cases, when the dataset is pre-processed and without preprocessing by using 5-fold cross validation. The authors didn't explain the pre-processing step applied on the dataset, they just mentioned that the noise was removed from the data. They reported that the decision tree J48 classifier has the highest accuracy rate being 73.82% without pre-processing, while the classifiers KNN (k = 1) and Random Forest showed the highest accuracy rate of 100% after pre-processing the data. Moreover, [PERSON] and [PERSON] [17] presented an application for diabetes prediction using three different ML algorithms including Random Forest, Decision Tree, and the Naive Bayes. The Pima Indian Diabetes dataset (PID) was used after pre-processing it. The authors didn't mention how the data was pre-processed, however they discussed the Information Gain method used for feature selection to extract the relevant features. They used only eight main attributes among 13 (see Table 14). In addition, they divided the dataset into 70% for training and 30% for testing. The results showed that the random forest algorithm had the highest accuracy rate of 94%. Furthermore, [PERSON] et al. [18] proposed a new integrated improved model of SVM and Naive Bayes for predicting the diabetes. The model was evaluated using a dataset collected from three different locations in Kosovo. The dataset contains eight attributes and 402 patients where 80 patients had type 2 diabetes. Some attributes utilized in this study (see Table 14) have not been investigated before, including the regular diet, physical activity, and family history of diabetes. The authors didn't mention whether the data was pre-processed or not. For the validation test, they split the dataset into 50% for each of the training and testing sets. The proposed combined algorithms have improved the accuracy of the prediction to reach 97.6%. This value was compared with the performance of SVM and Naive Bayes achieving 95.52% and 94.52%, respectively. In addition, [PERSON] and [PERSON] [19] used Decision Tree, SVM, and Naive Bayes classifiers to detect diabetes. The aim was to identify the classifier with the highest accuracy. The Pima Indian dataset was used for this study. The partition of the dataset is done by means of 10-folds cross-validation. The authors didn't discuss the data preprocessing. The performance was evaluated using the measures of the accuracy, the precision, recall, and the F-measure. The highest accuracy was obtained by the Naive Bayes, which reached 76.30%. [PERSON] et al. [20] used six different classifiers. The classifiers are J48, Multilayer Perceptron, HoeffdingTree, JRip, BayesNet, and RandomForest. The Pima Indian dataset was also utilized for this study. The authors didn't mention a preprocessing step, however, they employed two algorithms, GreedyStepwise and BestFirst, to determine the discriminatory attributes that help in increase the classification performance. Four attributes have been selected, namely body mass index, plasma glucose concentration, diabetes pedigree function, and age. A 10 fold-cross validation is applied to the dataset. The comparison between the classifiers was made based on the value of the precision, the recall, and the F-Measure. The result showed the precision value equals to 0.757, recall equals to 0.762, and F-measure equals to 0.759 using the Hoeffding Tree algorithm. This is the highest performance compared to the others.