## 2 Proposed Digital Health Platform
### Advanced Analytics
We ran the model with and without the variables, and there were no significant changes in the accuracy score, positive predicted value rate, and true positive rate. The training dataset was derived from a random sampling of 70% (13,831) of the extracted study population and the test sampling the remaining 30% (5928) to evaluate the model on the ground-truth that was never used for training. We ran the logistic regression model on the entire dataset to verify the accuracy score of the model. \begin{table}
\begin{tabular}{c c c} \hline
**Hypertension, Adults 20 and over—2007-2016** & \\ \hline \multirow{5}{*}{**Non Hypertensive**} & **Gender** & **Race** & **n** \\ \cline{3-4}  & \multirow{3}{*}{**Female**} & **Mexican American** & 1269 \\ \cline{3-4}  & & **Non-Hispanic Black** & 1674 \\ \cline{3-4}  & & **Non-Hispanic White** & **3674** \\ \cline{3-4}  & & **Other Hispanic** & 951 \\ \cline{3-4}  & & **Other Race—Including Multi-Racial** & 864 \\ \cline{3-4}  & \multirow{3}{*}{**Male**} & **Mexican American** & 1255 \\ \cline{3-4}  & & **Non-Hispanic Black** & 1599 \\ \cline{3-4}  & & **Non-Hispanic White** & **3714** \\ \cline{3-4}  & & **Other Hispanic** & 774 \\ \cline{3-4}  & & **Other Race—Including Multi-Racial** & 843 \\ \hline \multirow{5}{*}{**Hypertensive**} & **Female** & **Mexican American** & 205 \\ \cline{3-4}  & & **Non-Hispanic Black** & 420 \\ \cline{3-4}  & & **Non-Hispanic White** & **662** \\ \cline{3-4}  & & **Other Hispanic** & 149 \\ \cline{3-4}  & & **Other Race—Including Multi-Racial** & 114 \\ \cline{3-4}  & & **Mexican American** & 214 \\ \cline{3-4}  & & **Non-Hispanic Black** & 478 \\ \cline{3-4}  & & **Non-Hispanic White** & **670** \\ \cline{3-4}  & & **Other Hispanic** & 138 \\ \cline{3-4}  & & **Other Race—Including Multi-Racial** & 132 \\ \hline \end{tabular}
\end{table} 
Table 1: Number of samples by hypertensive class, gender, and race. The Logistic Regression model uses the logit function to express the relationship of the risk factors as:  
\[logit(p)=ln(\frac{p}{1-p})=\beta_{0}+\beta_{1}X_{1}+\beta_{2}X_{2}+...+\beta_{i}X _{i}\]  
The probability of success can be expressed as:  
\[p=\frac{e^{(\beta_{0}+\beta_{1}X_{1}+\beta_{2}X_{2}+...+\beta_{i}X_{i})}}{1+e^{ (\beta_{0}+\beta_{1}X_{1}+\beta_{2}X_{2}+...+\beta_{i}X_{i})}}\]  
where \(p\) is the predicted probability of having hypertension, \(X_{i}\) are the risk factors or independent variables, and \(\beta_{i}\) are the coefficients that are estimated by using the method of maximum likelihood and allow us to calculate the odds that, for every unit increase in \(X_{i}\), the odds of having hypertension changes by \(e^{\beta}\). * **A neural network approach to predict early neonatal sepsis:** We developed a non-invasive neural network classification model for early neonatal sepsis detection. The data used in this study are from Crecer's Hospital center in Cartagena-Colombia. A dataset of 555 neonates with (66%) of negative cases and (34%) of positive cases was used to train and test the model. The study results show a sensitivity of 80.32%, a specificity of 90.4%, precision on the positive predicted value of 83.1% in the test, sample and a calculated area under the curve of 0.925 (95% Confidence Interval [91.4; 92; 93.06]). The neural network architecture can be seen in Figure 3.