# Prediction of Post-Intubation Tachycardia Using Machine-Learning Models
## 3 Results
Parameters of each model were optimized through a grid search, for which the settings are described in Table 4. We used a computer with eight Central Processing Units (CPU) of i7-7700 3.6 GHz and two NVIDIA GeForce 1080 Ti. The machine learning models were implemented with Python3 language. The experimental results are summarized in Table 5, where the first row, \"All feature,\" refers to a 153-dimensional feature vector \(f_{input}\). The feature sets with feature selection algorithms (e.g., RFE-based, GI-based, and UST-based) gave better results than the \(f_{input}\). Among the six machine learning models, LR and SVM achieved \begin{table}
\begin{tabular}{l l} \hline \hline
**Models** & **Description** \\ \hline \multirow{8}{*}{Artificial neural network} & Common parameter : Activation (relu), Solver (adam), L2 penalty (0.001), \\  & Maximum iteration (200) \\  & Common structure : two hidden layers = [15; 20] \\  & All feature : L2 penalty = 0.1, One hidden layer with 10 nodes \\  & RFE-based : Initial parameter \\  & GI-based : Initial parameter \\  & UST-based : Activation = identity, Solver = sgd \\  & \(P\)-based : Activation = identity, Solver = lbfgs \\  & Hand-crafted : Activation = identity, Solver = lbfgs \\ \hline \multirow{8}{*}{Logistic regression} & Common parameter : Penalty (L2), C (1.0), Solver (liblinear) \\  & All feature : Penalty = L1, C = 0.1 \\  & RFE-based : Penalty = L1 \\  & GI-based : Penalty = L1 \\  & UST-based : Penalty = L1 \\  & \(P\)-based : C=10 \\  & Hand-crafted : Initial parameter \\ \hline Naive Bayes (GaussianNB) & Naive Bayes using Gaussian distribution \\ \hline \multirow{8}{*}{Random forest} & Common parameter : \# of estimators (10), Maximum depth (None), \\  & All feature : \# of estimators = 30, Maximum depth = 2, n\_jobs = -1 \\  & RFE-based : \# of estimators = 700, Maximum depth = 7 \\  & GI-based : \# of estimators = 700, n\_jobs = -1 \\  & UST-based : \# of estimators = 700 \\  & \(P\)-based : Maximum depth = 2 \\  & Hand-crafted : Initial parameter \\ \hline \multirow{8}{*}{Support vector machines} & Common parameter : Kernel (rbf), C (1.0), Maximum iteration (-1) \\  & All feature : Kernel = linear, C = 0.1 \\  & RFE-based : Initial parameter \\  & GI-based : Kernel = linear \\  & UST-based : Initial parameter \\  & \(P\)-based : Kernel = linear \\  & Hand-crafted : Kernel = linear \\ \hline \multirow{8}{*}{Extreme gradient boosting} & Common parameter : \# of estimators (100), Gamma (0), n\_jobs (1) \\  & All feature : \# of estimators = 20 \\ \cline{1-1}  & RFE-based : Initial parameter \\ \cline{1-1}  & GI-based : gamma = 0.1, n\_jobs = -1 \\ \cline{1-1}  & UST-based : Initial parameter \\ \cline{1-1}  & \(P\)-based : \# of estimators = 20 \\ \cline{1-1}  & Hand-crafted : Initial parameter \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Parameter settings for machine learning models. the best accuracy of 80.5% with hand-crafted features. As it is more critical if we miss post-intubation tachycardia, better sensitivity is preferable. In terms of the sensitivity, the LR will be the most effective because its recall of tachycardia (85.1%) was greater than that of the SVM. As shown in Figure 4, the LR model achieved a much better AUC with the hand-crafted features than with the GI-based features. Although the hand-crafted feature was not the best for some models (e.g., random forest), all six models generally achieved high performances with it.