## 2 Proposed Digital Health Platform
### Advanced Analytics
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Quantitative Obstetric Variable**} & \multirow{2}{*}{**Categories**} & \multicolumn{3}{c}{**Cases**} & \multicolumn{3}{c}{**Controls**} & \multirow{2}{*}{**X2**} & \multirow{2}{*}{_p_**-Value**} \\ \cline{2-2} \cline{5-8}  & **Mean** & **Median** & **SD** & **RIQ** & **Mean** & **Median** & **SD** & **RIQ** & _p_**-Value** \\ \hline \multirow{2}{*}{**Gestational age at the time of birth**} & 35.6 & 36.0 & 3.47 & 34–39 & 38.4 & 39.0 & 1.62 & 38–39 & 0.0001 \\ \hline \multirow{2}{*}{**Number of prenatal controls**} & 4.08 & 5.0 & 1.83 & 3.75–5.0 & 4.32 & 5.0 & 1.83 & 4–5.0 & 0.002 \\ \hline \multirow{2}{*}{**Number of pregnancies**} & 1.77 & 1.0 & 1.15 & 1.0–2.0 & 1.6 & 1.0 & 1.15 & 1–2.0 & 0.076 \\ \hline \multirow{2}{*}{**Number of births**} & 1.04 & 1.0 & 1.03 & 0–1 & 0.7 & 1.0 & 1.03 & 0–1 & 0.0001 \\ \hline \multirow{2}{*}{**Numbers of C-sections**} & 0.65 & 1.0 & 0.68 & 0–1 & 0.76 & 1.0 & 0.68 & 0–1 & 0.029 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Quantitative Obstetric variables in cases (186) and controls (369). \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Qualitative Obstetric Variable**} & \multirow{2}{*}{**Categories**} & \multicolumn{3}{c}{**Cases**} & \multicolumn{3}{c}{**Controls**} & \multirow{2}{*}{**X2**} & \multirow{2}{*}{_p_**-Value**} \\ \cline{3-4} \cline{6-7}  & & **N** & **\%** & **N** & **\%** & & \\ \hline \multirow{2}{*}{**Type of birth**} & \multirow{2}{*}{Vaginal C-Section} & 98 & 52.7 & 162 & 43.9 & \multirow{2}{*}{3.833} & \multirow{2}{*}{0.05} \\  & & C-Section & 88 & 47.3 & 207 & 56.1 & & \\ \hline \multirow{2}{*}{**IUGR Background**} & Yes & 5 & 2.7 & 13 & 3.5 & \multirow{2}{*}{0.275} & \multirow{2}{*}{0.6} \\  & No & 181 & 97.3 & 356 & 96.5 & & \\ \hline \multirow{2}{*}{**Assistance for prenatal control**} & Yes & 165 & 88.7 & 318 & 86.2 & \multirow{2}{*}{0.702} & \multirow{2}{*}{0.402} \\  & No & 21 & 11.3 & 51 & 13.8 & & \\ \hline \multirow{2}{*}{**Assistance for at least 4 prenatal control**} & Yes & 140 & 75.3 & 301 & 81.6 & \multirow{2}{*}{3.01} & \multirow{2}{*}{0.083} \\  & No & 46 & 24.7 & 68 & 18.4 & & \\ \hline \multirow{2}{*}{**Assistance for at least 5 prenatal control**} & Yes & 105 & 56.5 & 254 & 68.8 & \multirow{2}{*}{8.301} & \multirow{2}{*}{0.004} \\  & No & 81 & 43.5 & 115 & 31.2 & & \\ \hline \multirow{2}{*}{**Premature rupture of membrane with more than 18 hours**} & Yes & 95 & 51.1 & 17 & 4.6 & \multirow{2}{*}{165.7} & \multirow{2}{*}{0.00001} \\  & No & 91 & 48.9 & 352 & 95.4 & & \\ \hline \multirow{2}{*}{**Chorioamnionitis**} & Yes & 23 & 12.4 & 3 & 0.8 & \multirow{2}{*}{36.96} & \multirow{2}{*}{0.00001} \\  & No & 163 & 87.6 & 366 & 99.2 & & \\ \hline \multirow{2}{*}{**Premature membrane rupture with more than 6 hours**} & Yes & 161 & 86.6 & 194 & 52.6 & \multirow{2}{*}{61.96} & \multirow{2}{*}{0.0001} \\  & No & 25 & 13.4 & 175 & 47.4 & & \\ \hline \multirow{2}{*}{**Multiple Pregnacies**} & Yes & 2 & 1.1 & 10 & 2.7 & \multirow{2}{*}{0.39} & \multirow{2}{*}{0.353} \\  & No & 184 & 98.9 & 359 & 97.3 & & \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Qualitative Obstetric variables in cases (186) and controls (369). In terms of computational timing, It is difficult to evaluate the complexity and timing of a machine learning algorithm. However, based on the algorithmic complexity, we can measure the time performance in terms of its training time complexity using big O notation because the classification time of the models can vary depending on the stress in the computational performance and power. In terms of timing, the classification prediction with the trained models is less than 1 s. The time complexity of the logistic regression could be expressed as \(O((f+1)csE)\), where f is the number of features (+1 because of bias), \(c\) is the number of possible outputs, \(s\) is the number of samples, and \(E\) is the number of epochs to run.