# Prediction of Post-Intubation Tachycardia Using Machine-Learning Models
### Feature Selection
\begin{table}
\begin{tabular}{l c c c c} \hline \hline  & **All Cases** & **No Tachycardia** & **Tachycardia** & \(p\)**Value** \\  & **(n = 257)** & **(n = 207)** & **(n = 50)** & \\ \hline Age - yr & 58.2 (14.5) & 58.7 (14.2) & 56.2 (15.7) & 0.266 \\ Sex (Female) & 131 (51.0\%) & 106 (51.2\%) & 25 (50.0\%) & 0.878 \\ Height—cm & 161.2 (9.3) & 161.2 (9.4) & 161.5 (9.3) & 0.851 \\ Weight—kg & 64.6 (12.4) & 64.7 (12.3) & 64.1 (12.8) & 0.777 \\ BMI—kg/m\({}^{2}\) & 24.7 (3.5) & 24.8 (3.5) & 24.5 (3.7) & 0.588 \\ ASA classification - no. & & & 0.647 \\
1 & 72 (58.8\%) & 59 (28.5\%) & 13 (26.0\%) & \\
2 & 151 (28.0\%) & 119 (57.5\%) & 32 (64.0\%) & \\
3 & 34 (13.2\%) & 29 (14.0\%) & 5 (10.0\%) & \\ \hline Comorbidities - no. & & & & \\ Cardiovascular disease & & & & \\ Hypertension & 100 (38.9\%) & 81 (39.1\%) & 19 (38.0\%) & 0.883 \\ Atrial fibrillation & 6 (2.3\%) & 4 (1.9\%) & 2 (4.0\%) & 0.331 \\ Coronary artery disease & 11 (4.3\%) & 9 (4.3\%) & 2 (4.0\%) & 0.636 \\ Angina pectoris & 5 (1.9\%) & 5 (2.4\%) & 0 (0.0\%) & 0.336 \\ Congestive heart failure & 2 (0.8\%) & 1 (0.5\%) & 1 (2.0\%) & 0.352 \\ Respiratory disease & & & & \\ Asthma & 12 (4.7\%) & 8 (3.9\%) & 4 (8.0\%) & 0.187 \\ Chronic obstructive pulmonary disease & 4 (1.6\%) & 4 (1.9\%) & 0 (0.0\%) & 0.418 \\ Gastrointestinal disease & & & & \\ Hepatitis & 3 (1.2\%) & 2 (1.0\%) & 1 (2.0\%) & 0.479 \\ Liver cirrhosis & 7 (2.7\%) & 6 (2.9\%) & 1 (2.0\%) & 0.591 \\ Viral carrier & 7 (2.7\%) & 5 (2.4\%) & 2 (4.0\%) & 0.409 \\ Hepatitis B viral infection & 14 (5.4\%) & 10 (4.8\%) & 4 (8.0\%) & 0.280 \\ Renal disease & & & & \\ Chronic kidney injury & & & & \\
2 & 1 (0.4\%) & 1 (0.5\%) & 0 (0.0\%) & \\
3 & 6 (2.3\%) & 6 (2.9\%) & 0 (0.0\%) & \\
4 & 1 (0.4\%) & 1 (0.5\%) & 0 (0.0\%) & \\ End-stage renal disease & 2 (0.8\%) & 2 (1.0\%) & 0 (0.0\%) & 0.648 \\ Endocrine disease & & & & \\ Diabetes mellitus & 58 (22.6\%) & 50 (24.2\%) & 8 (16.0\%) & 0.216 \\ Thyroid disease & & & & 0.386 \\
1 & 3 (1.2\%) & 3 (1.4\%) & 0 (0.0\%) & \\
2 & 2 (0.8\%) & 1 (0.5\%) & 1 (2.0\%) & \\
3 & 13 (5.1\%) & 9 (4.3\%) & 4 (8.0\%) & \\ Hemoglobin A1c & 1.5 (2.9\%) & 1.6 (3.0\%) & 1.0 (2.4\%) & 0.223 \\ Neurologic disease & & & & \\ Cerebrovascular disease & 10 (3.9\%) & 8 (3.9\%) & 2 (4.0\%) & 0.612 \\ Dementia & 1 (0.4\%) & 1 (0.5\%) & 0 (0.0\%) & 0.805 \\ \hline Baseline & & & & \\ Systolic blood pressure—mmHg & 142.8 (23.7) & 143.3 (24.7) & 141.0 (19.3) & 0.546 \\ Mean blood pressure—mmHg & 102.7 (15.8) & 102.6 (16.4) & 102.8 (13.0) & 0.959 \\ Diastolic blood pressure—mmHg & 78.5 (11.5) & 77.9 (11.4) & 80.8 (11.6) & 0.11 \\ Heart rate—/min & 75.5 (15.6) & 73.3 (14.5) & 84.6 (17.1) & \(<\) 0.001 *** \\ \hline Noninvasive blood pressure—mmHg & & & & \\ Systolic & 123.4 (19.6) & 123.7 (20.2) & 122.0 (16.8) & 0.57 \\ Mean & 123.4 (19.6) & 123.7 (20.2) & 122.0 (16.8) & 0.57 \\ Diastolic & 69.9 (9.6) & 69.6 (9.8) & 71.0 (8.7) & 0.357 \\ \hline Heart rate—/min & 72.9 (12.6) & 70.1 (10.9) & 84.4 (12.8) & \(<\) 0.001 *** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Statistical characteristics of variables. The five feature sets were compared using various machine learning models: random forest (RF) [17], logistic regression (LR) [18], naive Bayes classifiers (NB) [19], support vector machine (SVM) [20], extreme gradient boosting (XGB) [21], and artificial neural networks (ANN) [22]. The ANN had two hidden layers, in which the first and second layers had 15 and 20 nodes, respectively.