## 4 Hybrid Prediction Model
### Outlier Detection Based on DBSCAN
However, for specificity, the C4.5 performed better, as the result achieved as much as 88.123% when compared to 82.553% in our proposed model. Overall, the proposed HPM achieved highest performance (accuracy up to 83.644%) as compared to other models. The attributes of dataset III are age and the current state whether the subject is diagnosed with hypertension, while the output class is whether the subject diagnosed with diabetes or not. In dataset III, the value of hypertension attribute depends on the input blood pressure of the subject. The subject has hypertension when systolic blood pressure \(\geq\)140 mmHg and diastolic blood pressure \(\geq\)90 mmHg. The proposed HPM successfully predicts the presence of diabetes given input, such as age and blood pressure from users. Finally, we can conclude that there are significant risk factors on diabetes, such as age and blood pressure/hypertension. A series of experiments were conducted on three datasets and concluded that the proposed HPM showed significant improvement when compared to other classification methods. The proposed HPM consists of DBSCAN-based outlier detection, SMOTE, and RF classifier. In terms of model \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & **Precision (\%)** & **Recall/Sensitivity (\%)** & **Specificity (\%)** & **F1 Score (\%)** & **Accuracy (\%)** \\ \hline SVM & 72.109 & 77.372 & 84.291 & 74.648 & 81.909 \\ MLP & 73.381 & 74.452 & 85.824 & 73.913 & 81.909 \\ Logistic Regression & 71.724 & 75.912 & 84.291 & 73.759 & 81.407 \\ Naive Bayes & 73.050 & 75.182 & 85.441 & 74.101 & 81.909 \\ C4.5 & 75.590 & 70.073 & 88.123 & 72.727 & 81.909 \\ Random Forest & 67.164 & 65.693 & 83.142 & 66.421 & 77.136 \\ Proposed HPM & 83.665 & 84.677 & 82.553 & 84.168 & 83.644 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Performance evaluation of classification model for dataset III. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & **Precision (\%)** & **Recall/Sensitivity (\%)** & **Specificity (\%)** & **F1 Score (\%)** & **Accuracy (\%)** \\ \hline SVM & 16.667 & 2.128 & 96.094 & 3.774 & 70.857 \\ MLP & 35.714 & 10.638 & 92.969 & 16.393 & 70.857 \\ Logistic Regression & 40 & 8.511 & 95.312 & 14.035 & 72 \\ Naive Bayes & 42.308 & 23.404 & 88.281 & 30.137 & 70.857 \\ C4.5 & 28.571 & 4.255 & 96.094 & 7.407 & 71.429 \\ Random Forest & 28 & 14.894 & 85.937 & 19.444 & 66.857 \\ [PERSON] et al. (2014) & - & 52.38 & 69.70 & - & - \\ Proposed HPM & 78.788 & 70.270 & 82.203 & 74.286 & 76.419 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Performance evaluation of classification model for dataset II. accuracy, the proposed HPM can be considered as the improvement of conventional RF as it performs consistently better than the traditional one. The proposed HPM utilizes DBSCAN-based outlier detection to remove the noise/outlier data. Furthermore, the imbalance class distribution appears in three datasets, and the SMOTE is applied to balance the dataset. The combination of DBSCAN-based outlier detection and SMOTE has improved the performance of the model. The details on the impact of DBSCAN-based outlier detection and SMOTE are presented in the following subsection. ### Impact of DBSCAN and SMOTE

In this part, the impact of DBSCAN-based outlier detection on the accuracy of RF classifier is presented. The detail size of original dataset before and after DBSCAN-outlier detection was presented in Table 4. The accuracy of RF classifier for the original dataset are 89.33%, 66.86%, and 77.14% for dataset I, II, and III, respectively. There were slightly improved for two datasets after the implementation of DBSCAN-based outlier detection. After removing the noise/outlier data, the result of RF classifier are 89.17%, 69.68%, and 79.11% for datasets I, II, and III, respectively. The result showed that by integrating DBSCAN-based outlier detection with the RF model, the average from the three datasets increased as much as 1.543% for model accuracy when compared to conventional RF.