## 2 Literature Review  
### Prediction Model for Diabetes and Hypertension  
Diabetes has turned into a worldwide pandemic that puts a grave load on healthcare systems, particularly in developing countries [4]. On a global stage, the total number of diabetic patients is estimated to increase from 171 million to 366 million in 2000 and 2030, respectively [39]. T2D is an advanced stage in which the body becomes stiff to normal effects of insulin and slowly loses the capacity to generate enough insulin in the pancreas. It is necessary for persons \(\geq\)45 years, with a BMI \(\geq\)25 kg/m\({}^{2}\), to experience screening to detect pre-diabetes and diabetes [2]. Furthermore, hypertension, which is usually referred as systolic blood pressure \(\geq\)140 mmHg and diastolic blood pressure \(\geq\)90 mmHg, is a normal long-term disease that presently impacts 77 million Americans [40; 41; 42]. It is a noteworthy risk element for the lethal cardiovascular diseases, developing heart failure in 91% of cases; it is present in 69% of persons who suffer their first heart attack and in 77% of those having their first stroke [41]. Past studies have shown firm positive relationships among blood pressure, danger of cardiovascular diseases, and mortality [43; 44]. Together, hypertension and diabetes are stroke risk factors, but they can be avoided if individuals take a healthy diet as well as physical exercise every day [45]. Therefore, in the future, a prediction model that notifies people on the chance of diabetes and hypertension is required, and it would permit them to take preemptive action. The machine learning algorithms can be used to diagnose diabetes and hypertension that is based on the present condition of patients. Several studies have shown a positive impact of the application of machine learning for diabetes classification. [PERSON] et al. proposed HPM for T2D [13]. The proposed model consists of a K-means algorithm to remove incorrectly classified instance and C4.5 to classify the diabetes dataset. The Pima Indian dataset and k-fold cross-validation are utilized. The result revealed that the HPM showed the highest accuracy, as high as 92.38%, among other methods. [PERSON] et al. utilized an HPM for predicting T2D [14]. The model consists of an improved K-means and the logistic regression model. The improved K-means algorithm was used to confiscate incorrect clustered data, later the logistic regression algorithm was used to classify the remaining data. The findings indicated that the proposed model demonstrated greater prediction accuracy as compared with past work. Previous literature compared the performance of logistic regression, artificial neural networks (ANNs), and decision tree models for anticipating diabetes or prediabetes employing common risk factors [15]. The dataset was gathered from Guangzhou, China, and 735 patients were validated as having diabetes or prediabetes, while 752 were normal controls. The findings indicated that the greatest classification accuracy as compared to other model is shown by decision tree model C5.0. Finally, past literature proposed a machine learning model to predict the prevalence of diabetes and hypertension, with a dataset having 13,647,408 medical records for diverse ethnicities in Kuwait [16]. The classification models, for example, logistic regression, K-Nearest Neighbors (KNN), Multi-factor Dimensionality Reduction (MDR), and Support Vector Machines (SVM), were used and indicated noteworthy finding on predicting diabetes and hypertension. Besides, the study inferred that ethnicity is a critical ingredient for anticipating diabetes. Furthermore, several studies have been conducted and revealed that the machine learning algorithms provide early prediction as well as treatment for hypertension. [PERSON] et al. investigated the advantage of machine learning for treatment of hypertension [17]. They used machine learning methods to distinguish determinants that add to the accomplishment of hypertension drug treatment on a massive set of patients.