# A Hybrid Intelligent Approach to Predict Discharge Diagnosis in Pediatric Surgical Patients
## 3 Experimental Results
### Main Processing  
With sample records meeting the new distribution of data provided by the KNN imputer, a subset of six features was selected through the SFS wrapper. The features included DI, ICD10, Medicine, TT_Medic, Complications, and D_Hospital, producing a _precision_ up to 82.4%. For data filtering, the best hyperparameters of the DBSCAN were explored on validation data using a search strategy based on random candidate combinations (provided by the ParameterSampler function). The hyperparameters found include \(eps=4.5285\), \(minsamples=9\), \(p=1\), and a cohesion of cluster of 87%; as a result, nine records were deleted from the original dataset. For a fair comparison, the _GridSearch_ algorithm was applied to all classifiers with partitions of data following 70% for training  
Figure 3: (**a**) Distribution of the age data using the K-nearest neighbor (KNN) imputer; and (**b**) the distribution of age data using simple imputer. and 30% of samples for test. The resulting hyperparameters for each classifier are shown in Table 3.  
The average performance of each classifier after a 10-fold cross-validation strategy for classifier evaluation and comparison is presented in Table 4, where the numbers in parenthesis represent the standard deviation for ten replications of the experiment. Bold numbers highlight the highest performance. Results in Table 4 reveal that the proposed XGBoost algorithm achieves the best performance in terms of both _accuracy_ and _F-measure_. These results are consistent with those presented in the literature for different applications. For instance, [PERSON] et al. (2018) mentions the XGBoost model as a robust algorithm to build predictive models when applied to predict the environmental effects around a mine. \begin{table}
\begin{tabular}{l l l} \hline \hline
**Technique** & **Hyperparameter** & **Values** \\ \hline \multirow{8}{*}{XGBoost} & max\_depth & \{4,5,6,7\} \\  & learning\_rate & \{0.001, 0.01, 0.1, 0.2, 0,3\} \\  & subsample & \{0.5, 0.6, 0.7, 0.8, 0.9, 1.0\} \\  & colsample\_bytree & \{0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0\} \\  & colsample\_bylevel & \{0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0\} \\  & min\_child\_weight & \{0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0\} \\  & gamma & \{0, 0.25, 0.5, 1.0\} \\  & reg\_lambda & \{0.1, 1.0, 5.0, 10.0, 50.0, 100.0\} \\ \hline \multirow{8}{*}{AdaBoost} & base\_estimator\_criterion & \{gini, entropy\} \\  & base\_estimator\_splitter & \{best, random\} \\ \cline{1-1}  & algorithm & SAMME.R, SAMME\} \\ \cline{1-1}  & n\_estimators & \{10, 100, 200, 250\} \\  & learning\_rate & \{0.05, 0.5, 1.5, 2.5\} \\ \hline \multirow{8}{*}{Gradient boosting} & loss & \{deviance\} \\  & learning\_rate & \{0.01, 0.025, 0.05, 0.075, 0.1, 0.15, 0.2\} \\  & min\_samples\_split & \{0.1,0.5,12\} \\  & min\_samples\_leaf & \{0.1,0.5,12\} \\ \cline{1-1}  & max\_depth & \{3,5,8\} \\ \cline{1-1}  & max\_features & \{log2,sqrt\} \\ \cline{1-1}  & criterion & \{friedman\_mse,mae\} \\ \cline{1-1}  & subsample & \{0.5, 0.618, 0.8, 0.85, 0.9, 0.95, 1.0\} \\ \cline{1-1}  & n\_estimators & \{10\} \\ \hline \multirow{8}{*}{Random bagging} & criterion & \{gini, entropy\} \\  & learning\_rate & \{0.01, 0.025, 0.05, 0.075, 0.1, 0.15, 0.2\} \\ \cline{1-1}  & min\_samples\_split & \{3,4,5,6,7\} \\ \cline{1-1}  & min\_samples\_leaf & \{1,2,3\} \\ \cline{1-1}  & random\_state & \{123\} \\ \cline{1-1}  & n\_jobs & \{-1\} \\ \cline{1-1}  & n\_estimators & \{10,15,20,25,30\} \\ \hline \multirow{8}{*}{CART} & max\_features & \{auto, sqrt, log2\} \\ \cline{1-1}  & min\_samples\_split & \{2,3,4,5,6,7,8,9,10,11,12,13,14,15\} \\ \cline{1-1}  & min\_samples\_leaf & \{1,2,3,4,5,6,7,8,9,10,11\} \\ \cline{1-1}  & random\_state & \{123\} \\ \hline \multirow{2}{*}{Voting ensemble} & lr\_C & \{1.0, 100.0\} \\ \cline{1-1}  & svm\_C & \{2,3,4\} \\ \hline \hline \end{tabular}
\end{table} 
Table 3: List of hyperparameters for each algorithm.