## 3 Dataset and Feature Selection
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Feature** & **Explanation** & **IG Rank** \\ \hline _is.obese_ & The subject is obese (yes, no) & 0.0203 \\ _wlr_ & Waist hip ratio & 0 \\ _hc_ & Hip circumference (cm) & 0 \\ _bmi_ & Body mass index (kg/m\({}^{2}\)) & 0 \\ _uc_ & Waist circumference (cm) & 0 \\ _age_ & Age (years) & 0 \\ id & Subject ID & 0 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The feature of dataset II and its Information Gain (IG) Rank. original dataset are removed and the class outcome is modified. Finally, the attributes of updated dataset consist of age, bp (blood pressure), and htn (hypertension), while the class is whether the subject is diagnosed with diabetes mellitus. The dataset consists of 261 tested negative (normal), 137 tested positive (diabetes), and two unlabeled data. The HPM is expected to foresee either the subject is diagnosed with diabetes based on risk factor, such as age and hypertension; thus, it can reveal the relationship between hypertension and diabetes. The details of dataset attributes with its rank are presented in Table 3.  
By utilizing the above datasets, it is expected to predict the diseases (i.e., diabetes and hypertension) and reveal their risk factors. In this study, the dataset I is utilized by proposed HPM to predict whether the subject is diagnosed with diabetes or not, while the dataset II is utilized to predict whether there is presence of hypertension on the subject or not. Finally, dataset III is utilized to reveal the relationship between the hypertension and diabetes. The proposed HPM is expected to reveal the presence of diabetes given the input risk factors, such as age and hypertension. Furthermore, these benchmark datasets have been utilized by previous machine learning-related studies, thus the performance comparison with previous studies can be presented and the proposed HPM is expected to improve the model accuracy. The data pre-processing acts a key part as it can improve the classifier accuracy. Feature selection is employed to choose subset of features that contributes considerably to the objective class. The objective of the feature selection technique is to enhance the accuracy, lessens the process length, and the cost computation [58; 59]. One method for selecting pertinent features from a dataset is to choose them based on their computed significance. Lastly, the unrelated features can be erased from the dataset. In this study, the Information Gain (IG) technique is applied to evaluate the significance of features from all the datasets [51]. The Weka version 3.6.15 software is utilized to evaluate the significant of features by using IG [60]. Based on the attributed ranking provided by IG, the final features are selected based on the highest ranked attributes and highlighted in italic font in the Tables 1-3 for dataset I, II, and III, respectively. The exception is made for dataset II as the rank only appears for the _is.obese_ attribute. Therefore, we have ignored IG rank result and instead followed a previous approach [19] by utilizing all of the features from the dataset except id (Subject ID) for dataset II.