## 4 Hybrid Prediction Model
### Outlier Detection Based on DBSCAN
Available online: [[http://ish-world.com/downloads/pdf/global_brief_hypertension.pdf](http://ish-world.com/downloads/pdf/global_brief_hypertension.pdf)]([http://ish-world.com/downloads/pdf/global_brief_hypertension.pdf](http://ish-world.com/downloads/pdf/global_brief_hypertension.pdf)) (accessed on 3 July 2018). * [PERSON] et al. (2016) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] CDC Grand Rounds: A Public Health Approach to Detect and Control Hypertension. _MMWR Morb. Mortl. Wkly. Rep._**2016**, _65_, 1261-1264. [CrossRef] [PubMed]* [PERSON] et al. (2015) [PERSON]; [PERSON]; [PERSON] Hypertension Prevalence and Control among Adults: United States, 2011-2014. _NCHS Data Brief_**2015**, _220_, 1-8. Available online: [[https://www.cdc.gov/nchs/data/databriefs/db220.pdf](https://www.cdc.gov/nchs/data/databriefs/db220.pdf)]([https://www.cdc.gov/nchs/data/databriefs/db220.pdf](https://www.cdc.gov/nchs/data/databriefs/db220.pdf)) (accessed on 3 July 2018). * [PERSON] et al. (2013) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; et al. Heart disease and stroke statistics--2013 update: A report from the American Heart Association. _Circulation_**2013**, _127_, 143-152. [CrossRef] [PubMed]
* [PERSON] et al. (2010) [PERSON]; [PERSON]; [PERSON] Hybrid prediction model for Type-2 diabetic patients. _Expert Syst. Appl._**2010**, _37_, 8102-8108. [CrossRef]
* [PERSON] et al. (2018) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Type 2 diabetes mellitus prediction model based on data mining. _Inform. Med. Unlocked_**2018**, _10_, 100-107. [CrossRef]
* [PERSON] et al. (2013) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Comparison of three data mining models for predicting diabetes or prediabetes by risk factors. _Kaoksiung J. Med. Sci._**2013**, _29_, 93-99. [CrossRef] [PubMed]
* [PERSON] et al. (2013) [PERSON]; [PERSON]; [PERSON] [PERSON]; [PERSON] Predictive models to assess risk of type 2 diabetes, hypertension and comorbidity: Machine-learning algorithms and validation using national health data from Kuwait--A cohort study. _BMJ Open_**2013**, \(3\), e002457. [CrossRef] [PubMed]
* [PERSON] et al. (2018) [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON]; [PERSON] Machine learning of big data in gaining insight into successful treatment of hypertension. _Pharmacol. Res. Perspect._**2018**, \(6\), e00396. [CrossRef] [PubMed]
* [PERSON] et al. (2017) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON]; [PERSON]; [PERSON]; [PERSON] [PERSON] The application of a decision tree to establish the parameters associated with hypertension. _Comput. Methods Programs Biomed._**2017**, _139_, 83-91. [CrossRef] [PubMed]
* [PERSON] et al. (2014) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Predicting Increased Blood Pressure Using Machine Learning. _J. Obes._**2014**, _2014_, 637635. [CrossRef] [PubMed]
* [PERSON] and [PERSON] (2015) [PERSON]; [PERSON] [PERSON] Comparison of classifiers for the risk of diabetes prediction. _Procedia Comput. Sci._**2015**, _69_, 132-142. [CrossRef]
* [PERSON] et al. (2017) [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON] Predicting diabetes mellitus using SMOTE and ensemble machine learning approach: The Henry Ford Exerclse Testing (FIT) project. _PLoS ONE_**2017**, 12, e0179805. [CrossRef] [PubMed]
* [PERSON] et al. (2018) [PERSON] [PERSON]; [PERSON] [PERSON]; [PERSON]; [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON]; [PERSON]; [PERSON] [PERSON] Using machine learning on cardiorespiratory fitness data for predicting hypertension: The Henry Ford Exerclse Testing (FIT) Project. _PLoS ONE_**2018**, _13_, e0195344. [CrossRef] [PubMed]
* [PERSON] et al. (2014) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Predicting changes in hypertension control using electronic health records from a chronic disease management program. _J. Am. Med. Inform.