# A Deep Learning Model for Estimation of Patients with Undiagnosed Diabetes
## 2 Materials and Methods
### Analysis Methods
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{3}{*}{**Variables**} & \multicolumn{2}{c}{**Development Dataset**} & \multicolumn{2}{c}{**Validation Dataset**} \\  & \multicolumn{2}{c}{**n = 11456**} & \multicolumn{2}{c}{**n = 4444**} & \\ \cline{2-5}  & **NG** & **IFG** & **UDG** & **NG** & **IFG** & **UDG** \\  & **n = 8311** & **b = 2781** & **n = 364** & **n = 3115** & **b = 147** \\ \hline Age (cyan) & 45.6 \(\pm\) 15.5 & 54.0 \(\pm\) 13.3 & 55.6 \(\pm\) 12.4 & 45.7 \(\pm\) 15.8 & 53.9 \(\pm\) 14.2 & 55.5 \(\pm\) 13.5 \\ Male (\(\infty\)) & 37.7 & 52.8 & 55.2 & 37.5 & 52.7 & 55.8 \\ Height (mm) & 162.9 \(\pm\) 9.0 & 163.6 \(\pm\) 9.2 & 163.3 \(\pm\) 9.7 & 163.2 \(\pm\) 9.1 & 163.8 \(\pm\) 9.3 & 163.5 \(\pm\) 9.1 \\ Weight (kg) & 61.6 \(\pm\) 11.4 & 66.9 \(\pm\) 12.2 & 69.4 \(\pm\) 13.5 & 62.1 \(\pm\) 11.7 & 67.5 \(\pm\) 12.7 & 71.5 \(\pm\) 14.1 \\ SBP (mmHg) & 114.3 \(\pm\) 15.8 & 122.2 \(\pm\) 16.2 & 124.3 \(\pm\) 15.6 & 114.9 \(\pm\) 15.2 & 123.0 \(\pm\) 16.0 & 128.0 \(\pm\) 17.8 \\ DBP (mmHg) & 74.1 \(\pm\) 9.9 & 77.7 \(\pm\) 10.5 & 78.8 \(\pm\) 10.6 & 74.5 \(\pm\) 9.4 & 78.3 \(\pm\) 10.3 & 79.7 \(\pm\) 11.9 \\ Hypertension (\(\infty\)) & 12.2 & 26.3 & 34.1 & 13.9 & 28.0 & 39.5 \\ BMI (kg)\({}^{\prime}\)) & 23.1 \(\pm\) 3.2 & 24.9 \(\pm\) 3.4 & 25.9 \(\pm\) 3.9 & 23.2 \(\pm\) 3.3 & 25.0 \(\pm\) 3.4 & 26.6 \(\pm\) 4.1 \\ WC (cm) & 78.9 \(\pm\) 9.4 & 85.1 \(\pm\) 9.2 & 88.2 \(\pm\) 9.7 & 80.2 \(\pm\) 9.6 & 86.0 \(\pm\) 9.3 & 90.5 \(\pm\) 9.6 \\ FHD (\(\infty\)) & 18.3 & 22.4 & 33.0 & 21.5 & 26.1 & 36.1 \\ Father (\(\infty\)) & 8.1 & 7.7 & 11.8 & 10.6 & 10.9 & 11.6 \\ Mother (\(\infty\)) & 8.8 & 10.8 & 16.8 & 9.7 & 12.6 & 15.7 \\ Sibling (\(\infty\)) & 3.9 & 7.6 & 12.4 & 4.6 & 7.6 & 13.6 \\ Smoker (\(\infty\)) & 35.0 & 46.5 & 51.9 & 34.1 & 48.2 & 47.6 \\ FPC (mg/dL) & 89.9 \(\pm\) 5.8 & 107.0 \(\pm\) 6.3 & 153.3 \(\pm\) 37.0 & 90.0 \(\pm\) 5.9 & 107.1 \(\pm\) 6.4 & 156.8 \(\pm\) 43.4 \\ TC (mg/dL) & 187.7 \(\pm\) 34.0 & 195.4 \(\pm\) 34.8 & 206.6 \(\pm\) 37.8 & 193.1 \(\pm\) 34.6 & 200.4 \(\pm\) 37.9 & 208.2 \(\pm\) 24.3 \\ TG (mg/dL) & 118.0 \(\pm\) 89.2 & 159.1 \(\pm\) 121.0 & 216.8 \(\pm\) 103.8 & 120.79 \(\pm\) 166.7 \(\pm\) 150.1 & 204.4 \(\pm\) 140.0 \\ HDL (mg/dL) & 53.0 \(\pm\) 12.2 & 48.8 \(\pm\) 11.4 & 46.7 \(\pm\) 11.2 & 53.4 \(\pm\) 13.3 & 49.11\(\pm\)12.2 & 46.5 \(\pm\) 11.0 \\ \hline \hline \end{tabular} Data are expressed as the number of patients (percentage) or mean + SD, MC, normal group; IFG, impaired fasting group; UDG, undiagnosed diabetes groups; SBP, systolic blood pressure; DBP, diastolic blood pressure; BMI, body mass index; WC, waist circumference; FPG, fasting plasma glucose; TC, total cholesterol; TG, triglyceride.  
\end{table} 
Table 1: Basic characteristics of participants for the development and validation dataset. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Variables** & **B** & **Odd ration (95\% CI)** & _P value_ \\ \hline Age & 0.34 & 1.03 (1.03–1.04) & \textless{}0.01 \\ Male gender & 0.55 & 1.74 (1.41–2.15) & \textless{}0.01 \\ Hypertension & 1.02 & 2.77 (2.22–3.47) & \textless{}0.01 \\ BMI & 0.17 & 1.18 (1.15–1.22) & \textless{}0.01 \\ WC & 0.07 & 1.08 (1.07–1.09) & \textless{}0.01 \\ FHD & 0.72 & 2.05 (1.64–2.57) & \textless{}0.01 \\ Smoke & 0.57 & 1.77 (1.44–2.19) & \textless{}0.01 \\ \hline \hline \end{tabular} Data are expressed as odds ratio between variables. BMI, body mass index; WC, waist circumference; FHD, family history of diabetes.  
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Table 2: Bivariate analysis of the deep learning model (DLM) performance evaluation. we applied [PERSON]'s [35] optimization algorithm, which is considered one of the best results and is faster than others [28; 36]. To avoid overfitting, we used a dropout regularization technique that can avoid learning spurious features at hidden nodes. It has been shown that this method can provide a significant improvement in the generalization performance of the artificial neural network model, and that it is computationally cheap [31].