## 2 Literature Review
### Prediction Model for Diabetes and Hypertension
The result showed that a fully connected neural network could achieve AUC as much as 0.82. The result of their study showed that machine learning algorithms can provide the hypertension treatment with combinations of three or four medications. [PERSON] et al. built up a decision tree model to distinguish the risk factors that are related to hypertension [18]. A dataset comprising of 9078 subjects was part to 70% as training set and 30% as the testing dataset to assess the performance of the decision tree. Two models are proposed based on different risk factors. The result showed that the accuracy of the decision tree for both models could be as much as 73% and 70%, respectively. The finding is assumed to distinguish the risk factors that are related to hypertension that may be utilized to create programs for hypertension management. Finally, [PERSON] et al. presented the Classification and Regression Tree (CART) to predict hypertension based on several factor such as body mass index (BMI), waist (WC), and hip circumference (HC), and waist hip ratio (WHR) [19]. The finding demonstrates that, for women, BMI, WC, and WHR is the blend that creates the best prediction, while for men, BMI, WC, HC, and WHC are the topmost risk factors. Random Forest is an ensemble prediction technique by amassing the finding of individual decision trees [46]. Generally, Random Forest works by utilizing the bagging method to generate subsets of training data. For each training dataset, a decision tree algorithm is utilized. Lastly, the prediction results are acquired from the model (most frequent class) of every decision tree in the forest. A study regarding RF for early prediction of diabetes as well hypertension has been conducted and shown significant results. [PERSON] et al. utilized Random Forest as a classifier for diabetes risk prediction [20]. The dataset was gathered from 30,122 persons in Sawanpracharak Regional Hospital, Thailand, between 2012 and 2013. The features comprise of medical information for example BMI, age, weight, height, blood pressure, a history of diabetes, and hypertension in the family, gender, and liquor and smoking patterns. The findings manifest that RF performance is excellent as compared to rest of algorithms. Finally, [PERSON] et al. used an ensembling strategy that consolidated three decision tree classification strategies (RF, Naive Bayes [NB] Tree, and Logistic Model Tree [LMT]) for foreseeing the diabetes [21]. The finding demonstrated that the performance of the predictive model has accomplished topmost accuracy for anticipating incident diabetes using cardiorespiratory fitness data among models. Furthermore, RF also showed a significant result for hypertension prediction. [PERSON] et al. evaluated and compared the performance of various machine learning methods on foreseeing the people in danger of growing hypertension [22]. The dataset utilized data of 23,095 patients at Henry Ford Health Systems from 1991 to 2009. Six machine learning methods were researched: LogitBoost (LB), Bayesian Network classifier (BN), Locally Weighted Naive Bayes (LWB), ANN, SVM, and Random Tree Forest (RTF). The result showed that the RTF model had the best performance (AUC = 0.93) among the machine learning methods that were investigated in this study. Finally, [PERSON] et al. utilized the RF classifier as a model for transitions in hypertension control [23]. The dataset consisted of 1294 patients with hypertension at the Vanderbilt University Medical Center. The result showed that proposed RF accomplished exact forecast of change points of hypertension control status. The result of their study is expected to be used for personalized hypertension management plans. Existing studies show that the RF can be utilized for early prediction of diabetes as well as hypertension with high classification accuracy. However, several studies have revealed that the outlier data as well as imbalanced datasets are challenging problems in classification, as they can reduce the system performance.