## 4 Hybrid Prediction Model
### Outlier Detection Based on DBSCAN
The final end-user application, such as an Android app, collects the vital signs data from sensor device: i.e., weight scale through Bluetooth Low Energy (BLE) communication. The user weight data from BLE-based sensor device is combined with other risk factors some of which are DOB (date of birth), height, HC (hip circumference), and WC (waist circumference) are sent to the Representational State Transfer (REST API) to be stored in a secure remote server. As the count of devices gathering health data of patient grows, the chances of using new kind of applications that can handle the input of big amounts of health data (big data), such as NoSQL database, also grows. The proposed IoT-based Health-care Monitoring system utilized NoSQL MongoDB to store the user data, including their detail of health condition. Finally, the proposed HPM is utilized to predict \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & **Estimated Distribution of** & **\% SMOTE** & **\# Minority Cases** & **\# Majority Cases** & **Total Number** \\  & **Minority Cases** & **Increase** & **(*Yes” Class)** & **(*No” Class)** & **of Instance** \\ \hline \multirow{4}{*}{I} & 0.1 & 0 & 48 & 303 & 351 \\  & 0.2 & 70 & 76 & 303 & 379 \\  & 0.3 & 170 & 129 & 303 & 432 \\  & 0.4 & 350 & 216 & 303 & 519 \\  & 0.5 & 500 & 288 & 303 & 591 \\ \hline \multirow{4}{*}{II} & 0.2 & 0 & 37 & 118 & 155 \\  & 0.3 & 50 & 55 & 118 & 173 \\  & 0.4 & 130 & 85 & 118 & 203 \\  & 0.5 & 200 & 111 & 118 & 229 \\ \hline \multirow{4}{*}{III} & 0.3 & 0 & 124 & 235 & 359 \\  & 0.4 & 50 & 186 & 235 & 421 \\ \cline{1-1}  & 0.5 & 100 & 248 & 235 & 483 \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Distribution of minority cases. the presence of hypertension and diabetes by providing risk factors input by the user. The result of prediction is delivered to individual's smartphone app. Figure 5b shows the weight data from BLE-based sensor device is combined with other risk factors that are manually defined by the user. The sensor data from BLE-based weight scale (i.e., Mi Scale) is delivered to the Android app by BLE communication. BLE is the right option for sending sensor data wirelessly while keeping low power consumption [65; 66; 67]. Communication between the BLE-based sensors and a smartphone is explained with the help of Generic Attributes (GATT) [68]. A prototype of an Android app was developed to retrieve the weight data from BLE-based sensor devices to smartphone. By pressing the \"send\" button, the input user risk factors are stored in the remote server, and the proposed HPM is triggered to predict the presence of hypertension as well as diabetes for the user. Figure 5c shows the interface of the app when the user receives the prediction from HPM. By utilizing the IoT-based Health-care Monitoring System, the history of user health data can be presented. Also, the prediction results from the proposed HPM can be accessed by user through their Android app; thus, it is expected to help users in finding the danger of diabetes and hypertension efficiently at initial phase. ## 6 Conclusions

The present study proposed HPM by combining DBSCAN-based outlier detection, SMOTE, and RF classifier. The proposed model is believed to help users to find the danger of diabetes and hypertension at the initial phase. Three datasets that are related with diabetes and hypertension are utilized in this study. The HPM is applied for dataset I, to foresee the existence of diabetes given input of several risk factors. Dataset II reveals risk factor for hypertension, and the proposed HPM is expected to predict the presence of hypertension. Finally, dataset III revealed the relationship

Figure 5: Internet of Things (IoT)-based Health-care Monitoring System (**a**); input of risk factors (**b**), and the proposed HPM model predicts the presence of hypertension as well as diabetes (**c**). between age, hypertension, and diabetes.