## 4 Hybrid Prediction Model
### Outlier Detection Based on DBSCAN
The result showed that the proposed HPM outperformed the conventional classification models as well as models that are presented from previous studies with an accuracy up to 92.555%, 76.419%, and 83.644% for datasets I, II, and III, respectively. In addition, the IG technique can be applied to evaluate the significant of features from all datasets; thus, the highest risk factors of diabetes as well as hypertension can be extracted. Furthermore, the result of this study also revealed a significant relationship between the age, hypertension/blood pressure, and diabetes as presented in dataset III. The proposed HPM successfully detected the presence of diabetes given age and blood pressure as inputs. Therefore, the age and blood pressure can be considered as high-risk factors for detecting diabetes. The proposed HPM could be integrated with an IoT-based Health-care Monitoring System. The IoT-based Health-care Monitoring System utilizes an Android app to gather the vital signs data (i.e., weight data) from sensor devices through Bluetooth Low Energy (BLE) communication. The data is then combined with other risk factors and stored in a remote server, which utilizes NoSQL MongoDB, so that the voluminous incoming health data can be handled efficiently. Finally, the IoT-based Health-care monitoring system provides the prediction result from the proposed HPM and transmits it to the user's Android app; thus, it would assist users in finding the danger of diabetes and hypertension in efficient way. The comparison with other prediction models, as well as evaluation of other clinical datasets, needs to be considered in the near future. Furthermore, once the model validation is performed in other datasets, other risk factors that are affecting hypertension as well as diabetes can be revealed. [PERSON] conceived and designed the experiments; [PERSON] and [PERSON] performed the experiments; [PERSON] analyzed the data; [PERSON] and [PERSON] wrote the paper. This research was financially supported by the Ministry of Trade, Industry and Energy (MOTIE) and Korea Institute for Advancement of Technology (KIAT) through the International Cooperative R&D program (N0002301); 2018 Research Fund Program (Strategy Research Group) through Dongguk University. The authors declare no conflict of interest. ## References

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