## 3 Dataset and Feature Selection  
This section explains the dataset description and feature selection procedures. In order to investigate how the diabetes and hypertension can be predicted in early stage, this study was conducted on three different sources. The proposed HPM is applied to the three different dataset and is expected to generalize the robust classifier. The datasets on diabetes, hypertension, and Chronic Kidney Disease (CKD) are considered as dataset I, II, and III, respectively. Dataset I was provided by Dr [PERSON], Department of Medicine, University of Virginia School of Medicine [54; 55]. The data contained 403 instances. The subjects were interviewed to understand the prevalence of obesity, diabetes, and other cardiovascular risk factors in central Virginia for African Americans. The original features were 19 features without class variable. We defined the class variable as whether the subject is diagnosed with diabetes or not. This decision is based on the Glycosylated Hemoglobin of the subject, if the value >7.0 is diagnosed as diabetes, otherwise as normal. Based on this scenario, the updated dataset consists of 330 negative (normal) subjects and 73 positive (diabetes) subjects. The proposed HPM is expected to foresee that either the subject is diagnosed with diabetes or is not given several inputs of risk factors. The detail of dataset attributes with its rank is presented in Table 1.  
Dataset II is provided by [PERSON] et al. and it is utilized to reveal the relationship of increased blood pressure by BMI, WC, and HC, and WHR on male subject [19; 56]. Original dataset consists of nine features with one output class. We have removed the attributes Systolic Blood Pressure (SBP) and Diastolic Blood Pressure (DBP) due to its similarity with output class. The dataset consists of 175 male subjects, of whom 128 tested negatives (regular/normal) and 47 tested positive (hypertension). The hypertension is classified when the systolic blood pressure of subject >140 mmHg. By utilizing this dataset, the HPM is expected to foresee whether the subject is diagnosed with hypertension or not given several features, such as obesity, WHR, HC, BMI, WC, and age. The detail of dataset attributes with their ranks is presented in Table 2.  
Finally, dataset III is provided by Dr. [PERSON], M.D., D.M, from Apollo Hospitals, Tamilnadu, India [57]. The dataset originally has 24 features with 400 instances, where its class is either the subject is diagnosed with chronic kidney disease (CKD) or not. However, our study is focusing on the relationship between hypertension and diabetes; thus, most of the features from \begin{table}
\begin{tabular}{c c c} \hline \hline
**Feature** & **Explanation** & **IG Rank** \\ \hline _stab.glu_ & Stabilized Glucose (mg/dL) & 0.24978 \\ _age_ & Age (years) & 0.076107 \\ _ratio_ & Cholesterol/ High Density Lippproteins (HDL) Ratio & 0.044198 \\ _uust_ & Waist (inches) & 0.035667 \\ _chol_ & Total Cholesterol (mg/dL) & 0.034227 \\ _bp.1s_ & First Systolic Blood Pressure (mmHg) & 0.030449 \\ _frame_ & A factor with levels (small, medium, large) & 0.014988 \\ _location_ & Location of subject (Buckingham, Louisa) & 0.002732 \\ _gender_ & Gender of subject (male, female) & 0.000697 \\ hdl & High Density Lippoprotein (mg/dL) & 0 \\ time.ppm & Postprandial time when labs were drawn (minutes) & 0 \\ height & Height (inches) & 0 \\ hip & Hip (inches) & 0 \\ bp.2d & Second Diastolic Blood Pressure (mmHg) & 0 \\ bp.2s & Second Diastolic Blood Pressure (mmHg) & 0 \\ bp.1d & First Diastolic Blood Pressure (mmHg) & 0 \\ weight & Weight (pounds) & 0 \\ id & Subject ID & 0 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The feature of dataset I and its Information Gain (IG) Rank.