## 4 Hybrid Prediction Model
### Outlier Detection Based on DBSCAN
The SMOTE technique is applied to randomly generate new instances of the minority class from the nearest neighbors of the minority class sample, so that it would increase the number of minority cases. In the present study, SMOTE was employed for every dataset with different percentage to increase the balance of datasets; they are 500%, 200%, and 100% for dataset I, II, and III, respectively. After SMOTE implementation, the total instance of minority cases increases, and the updated datasets I, II, and III become more balanced, with 48%, 48.47%, and 51.35% minority cases, respectively. The application of SMOTE for all of the datasets is performed by utilizing Weka Software version 3.6.15 [60]. The detail impact of SMOTE increase can be seen in Table 5.

The SMOTE ensures that when generating the new artificial data, it will follow the distribution from the original dataset. Figure 3 showed the distribution of all dataset and the data is presented based on the attribute Age. For each dataset, the distribution of age for \"Yes\" and \"No\" classes follow normal distribution. Figure 3a showed the distribution of age in minority cases (\"Yes\" class) before SMOTE implementation and it showed less amount of data compared to majority cases (\"No\" class). After SMOTE is applied to dataset I, the number of instance in minority cases (class \"Yes\") increases, and the updated dataset becomes balanced (Figure 3b). The SMOTE implementation keeps the originality of dataset pattern as Figure 3b showed that, in updated dataset I, the minority cases still follow the original distribution (i.e., normal distribution). The same result applied in dataset II as the similar distribution found in the original dataset (Figure 3c) and updated dataset after SMOTE implementation (Figure 3d). A similar pattern appears in dataset III, as the updated dataset (Figure 3f) still follows the original pattern of the old dataset (Figure 3e). SMOTE algorithm uses oversampling \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & **\# Instance** & **\# Instances (After** & \multirow{2}{*}{_MinPts_} & \multirow{2}{*}{_eps_} & \multirow{2}{*}{**\# Outlier Data**} & \multirow{2}{*}{**\# Normal Data**} \\  & **(Original)** & & & & & \\ \hline I & 403 & 384 & 5 & 36 & 33 & 351 \\ II & 175 & 175 & 5 & 9 & 20 & 155 \\ III & 400 & 377 & 5 & 6 & 18 & 359 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The result of DBSCAN-based outlier detection. Figure 2: Optimal _eps_ value and outliers are presented respectively for each dataset I (**a,b**); dataset II (**c,d**) and dataset III (**e,f**). where synthetic instances are added, which are \"close to\" the given sample of minority cases; thus, it maintains the originality the distribution of dataset. The conventional classification algorithms aim to minimize the number of errors that are made during learning process. Hence, when the dataset is balanced it is expected to enhance the classifier accuracy. Figure 3: Attribute Age before and after SMOTE implementation are presented, respectively, for each dataset I (**a**,**b**); II (**c**,**d**); and, III (**e**,**f**). ### Random Forest

The RF algorithm is a type of classification method that is formed via combining decision trees. Past study described a randomization approach that works way better with bagging or random space method [46]. The randomization introduced by bootstrap sampling of the original data and at the node level when growing the tree. RF chooses just a random subset of factors at every node and uses them as contenders to locate the best split for the node. The generation of each tree in RF is presented in Algorithm 2.