## 4 Hybrid Prediction Model
### Outlier Detection Based on DBSCAN
The improved K-means algorithm was used to eliminate incorrectly clustered data; afterwards, the logistic regression algorithm was applied to classify the remaining data. The feature selection is different compared to our study, as their study utilized 12 significant attributes and the decision was based on the comparison with the attributes of the Pima Dataset [62] and some clinical experience. Our study utilized IG for feature selection, and finally decided that nine significant attributes are utilized to build the classifier model. Overall, we can conclude that the proposed HPM perform better with regard to model accuracy when compared with other models. In order to validate the prediction accuracy and adaptability of present model, the hypertension dataset (dataset II) that was provided by [PERSON] et al. was used [19]. The classification models are expected to predict whether the subject is diagnosed with hypertension or not given several features, \begin{table}
\begin{tabular}{c c c} \hline \hline  & **Predicted as \"Yes\"** & **Predicted as \"No\"** \\ \hline Actual “Yes” & True Positive (TP) & False Negative (FN) \\ Actual “No” & False Positive (FP) & True Negative (TN) \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Different outcomes of two-class prediction. \begin{table}
\begin{tabular}{c c} \hline \hline \multicolumn{1}{c}{**Performance Metric**} & **Formula** \\ \hline Precision & \(\text{TP}/(\text{TP}+\text{FP})\) \\ Recall/Sensitivity & \(\text{TP}/(\text{TP}+\text{FN})\) \\ Specificity/True Negative Rate & \(\text{TN}/(\text{TN}+\text{FP})\) \\ F1 Score & 2 * (Precision * Recall)/(Precision + Recall) \\ Accuracy & \((\text{TP}+\text{TN})/(\text{TP}+\text{TN}+\text{FP}+\text{FN})\) \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Performance metrics for the classification model. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{1}{c}{**Method**} & **Precision (\%)** & **Recall/Sensitivity (\%)** & **Specificity (\%)** & **F1 Score (\%)** & **Accuracy (\%)** \\ \hline SVM & 88.235 & 41.096 & 98.788 & 56.075 & 88.337 \\ MLP & 74.545 & 56.164 & 95.757 & 64.062 & 88.586 \\ Logistic Regression & 84.783 & 53.425 & 97.879 & 65.546 & 89.826 \\ Naive Bayes & 73.333 & 60.274 & 95.151 & 66.165 & 88.834 \\ C4.5 & 68.421 & 53.425 & 94.545 & 60 & 87.097 \\ Random Forest & 78.846 & 56.164 & 96.667 & 65.6 & 89.33 \\ [PERSON] et al. (2018) & 91.6 & 96.4 & - & - & 90.7 \\ Proposed HPM & 91.497 & 93.403 & 91.749 & 92.440 & 92.555 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Performance evaluation of classification model for dataset I.

such as Obesity, WHR, HC, BMI, WC, and Age. Table 9 shows the detail performance of proposed HPM as well as other conventional classification models, such as SVM, MLP, Logistic Regression, Naive Bayes, C4.5, and RF. The findings revealed that the proposed HPM excelled traditional models with respect to precision, recall, F1 score, and accuracy of the model. Furthermore, the proposed HPM is compared with past work by [PERSON] et al., which used Classification and Regression Tree (CART) to predict the hypertension [19]. Regarding the feature selection, we have followed a previous scenario [19] by utilizing all of the features from the dataset. The proposed HPM showed better performance, as the recall and specificity are 70.270% and 82.203%, respectively, when compared the previous study (52.38% and 69.70%). Overall, we can conclude that the proposed HPM achieved highest performance (up to 76.419%) as compared to other models. Finally, the proposed HPM is compared with conventional classification algorithms and applied to the dataset III to reveal the relationship between age, hypertension, and diabetes. Table 10 showed the detail performance of HPM as well as other conventional classification models, such as SVM, MLP, Logistic Regression, Naive Bayes, C4.5, and RF. The result showed that the proposed HPM outperformed traditional model as much as 83.665%, 84.677%, 84.168%, and 83.644% for precision, recall, F1 score, and accuracy, respectively.