## 2 Proposed Digital Health Platform
### Advanced Analytics  
The analytic data component consists of two areas: The first area is the BI models we develop for tactical, operational, and strategic decisions. The second area comprehends several prediction models that need to be developed. Currently, there are two prediction models developed by the authors of this paper to support population health management, specifically the diagnosis of sepsis and hypertension prediction [24; 25]. These insights assist providers in the detection and tracking of chronic diseases. The machine learning component is used to build, test, consume, and deploy predictive analytic models on-demand and as requested for the organization. The platform provides self-service dashboards and visualizations that use data from the repositories to drive the decision-making process. The machine learning application layer is one of the essential layers of this platform. Once the data are integrated, aggregated, and normalized in the system, the platform offers a tool to provide knowledge management through the business intelligence interface providing data analysis, design, and training of machine learning models, as well as development and management of results-based care indicators or population health management. The platform provides a tool where clinicians, researchers, and scientists can mine the data and get valuable information. Machine learning models can be trained and customized in preconfigured data domains, allowing the storage of the results for future use. Data researchers and scientists can develop advanced tools to obtain information and value of the data stored in the solution, taking advantage of the model design, training, and validation component. We briefly present the predictive models implemented in the platforms. * **Machine Learning Classification for a Hypertensive Population:** This prediction model evaluates the association between gender, race, BMI (Body Mass Index), age, smoking, kidney disease, and diabetes using logistic regression. Data were collected from NHANES datasets from 2007 to 2016 to train and test the model, a dataset of 19,709 samples with (83%) non-hypertensive individuals and (17%) hypertensive individuals. The results show a sensitivity of 77%, a specificity of 68%, precision on the positive predicted value of 32% in the test sample, and a calculated AUC of 0.73 (95% CI [0.70-0.76]). The model used to estimate the probability that a person will belong to the hypertensive or non-hypertensive class is: \[p=\frac{e^{(\beta_{0}+\beta_{1}gender+\beta_{2}age+\beta_{3}rac+\beta_{4}bmi+ \beta_{5}kidney+\beta_{6}smoke+\beta_{7}diabetes)}}{1+e^{(\beta_{0}+\beta_{1}gender+ \beta_{2}age+\beta_{3}race+\beta_{4}bmi+\beta_{5}kidney+\beta_{6}smoke+\beta _{7}diabetes)}}\]  
We used the logistic regression classification model in this experiment to evaluate the importance of the risk factor variables and their relationship with the prevalence of hypertension among a nationally representative sample of adults \(\geq\)20 years in the United States (n = 19,759). The distribution of the samples by hypertensive patients, gender, and race is shown in Table 1.  
We computed chi-square test between each independent variable and the dependent variable to indicate the strength of evidence that there is some association between the variables. Chi-square was selected due to the categorical form of the data used in the model, and it is considered one of the best methods to estimate the dependency between the class and the features when the feature can take a fixed number of possible values that belong to a group or nominal category. Table 2 shows the \(p\)-value for each variable; the null hypothesis is reject for any \(p\leq\) 0.05, while the null hypothesis is not rejected when \(p>\) 0.05. \(p\)-values for the variables GENDER, BMIRANGE_1, BMIRANGE_3, and KIDNY_2 are not statistically significant at 0.05 alpha level; the clinical importance of these variables in the model for interpretation allows us to include them.