## 4 Hybrid Prediction Model
### Outlier Detection Based on DBSCAN
The detailed impact of DBSCAN-based outlier detection on improving the accuracy of RF classifier can be seen in Figure 4a. Overall, we can conclude that by integrating DBSCAN-based outlier detection on the RF classifier, it will enhance the model accuracy. Furthermore, we combined the result from DBSCAN-based outlier detection with SMOTE, and the model accuracy is presented for RF Classifier. Table 11 showed the detail of estimation of distribution of minority cases for three datasets. The estimated distribution of minority cases is defined as total number of minority cases over total number of instances. The original size of dataset I, II, and III after outlier removal from DBSCAN are 351, 155, and 359, respectively. As increasing the distribution of minority cases, the number of instance of minority class also increase. Originally, dataset I is an imbalanced dataset where the minority cases (the subjects who diagnoses as diabetes) are 48 instances, while the majority cases (normal subjects) are 303 instances. After SMOTE implementation with 500% increase on minority cases, the updated dataset is becoming more balance with its minority cases is 288 (out of 591). The imbalanced dataset is also present in datasets II and III, in which numbers of minority cases are 37 (out of 155) and 124 (out of 359). By applying SMOTE with 200% and 100% increases, the updated dataset II and III become more balanced, where numbers of minority cases are 111 (out of 229) and 248 (out of 483). Figure 4: Impact of outlier elimination (**a**) and distribution of minority cases (**b**) on model accuracy. Finally, the model accuracy of DBSCAN-based outlier detection with different percent of SMOTE increase is presented for RF classifier. Figure 4b manifests the model accuracy on different distribution of minority cases. For all three datasets, the small increasing of distribution of minority cases will slightly reduce the model accuracy. However, once the datasets achieve balance condition (when the distribution of minority cases approximately close to 0.5), the model accuracy of RF classifier presents its best performance. As average from three dataset, by applying SMOTE, there was increasing as much as 4.885% for the model accuracy when compared to conventional RF without SMOTE integration. After SMOTE integration, the RF classifier can achieve the model accuracy by up to 92.555%, 76.419%, and 83.644% for dataset I, II, and III, respectively. Overall, we can conclude that integrating DBSCAN-based outlier detection, SMOTE for balancing the dataset and RF for classifier model will improve the accuracy of the model. ### Managerial Implications

[PERSON] et al. revealed that recent technologies, such as IoT, big data, cloud computing, novel biosensors, and machine learning can perform significant part in improving the diabetes management [38]. The previous studies have shown significant results with the implementation of IoT in healthcare systems. [PERSON] et al. proposed an IoT-based Information System for Healthcare Applications that enables the localization of a monitored person [63]. The proposed system successfully categorizes present activities of patients as normal, suspicious, or dangerous, which are utilized to inform the healthcare staff about potential problems. [PERSON] et al. proposed an IoT System for the remote monitoring of patients at home and utilized Personal Healthcare Devices (PHDs) that sense and calculate persons' biomedical signals [64]. The proposed system informs medical staffs when the patients encounter emergency situations in real-time. Due to successful IoT implementation from previous studies, the results of our study also could be applied to IoT-based Health-care Monitoring Systems. Figure 5a showed the proposed IoT-based Health-care Monitoring that can be utilized by individuals to record their risk factors as well as predicting the presence of hypertension and diabetes.