# Hospital Readmission and Length-of-Stay Prediction Using an Optimized Hybrid Deep Model
## 4 Experimental Results
### Performance Analysis  
After preprocessing, we divide each dataset into train, validation, and test. For better evaluation of the proposed model, we use k-fold cross-validation [51]. Here, we consider K as 10. A single fold acts as a test set, while the remaining nine folds are used as the training set. Finally, the results are averaged to represent a single estimation. The model was trained using the Tesla P100 graphics processing unit. The runtime for reaching the desired result differed based on the dataset and the number of classes for prediction. For MIMIC-III and diabetes datasets, the runtime to train the model was between 3 and 4 days, whereas for LOS prediction on the COVID-19 dataset, the runtime was about 6 h. To compare the proposed model, we used VGG16, ResNet, GoogLeNet [32], LR [17], RF [17], to XGB [20]) and SVM as the benchmarks. Furthermore, we implemented a combination of CNN and LR, CNN and RF, CNN and XGB, CNN and SVM, and a semi-supervised generative adversarial network (SGAN) model. We used convolutional layers to combine CNN with other ML methods as feature extractors and ML models as classifiers [52]. SGAN uses the CNN model achieved by GAOCNN as a generator and a multi-layer perception with three hidden layers and 128, 64, and 23 units, respectively, as discriminator [53]. We just converted the structure of the 2D convolutional layer of the mentioned model into 1D convolutional to match the structure of healthcare data. Table 3 indicates the performance of the readmission prediction using the proposed model (GAOCNN) and the benchmark models for diabetic patients. As can be seen, the GAOCNN outperforms all benchmarks. Figure 6: Distributions of the LOS in the original and balanced datasets: (**a**) Diabetes, (**b**) COVID-19, (**c**) MIMIC-III. The classification results of the LOS for diabetic, COVID-19, and ICU patients are shown in Tables 4-6, respectively. As can be seen, the performance of the GAOCNN is higher than all benchmarks for the LOS prediction in all diseases. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & **Accuracy** & **Sensitivity** & **Specificity** & **F1-Measure** & **Precision** \\  & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** \\ \hline GAOCNN & 89.0 & 89.8 & 97.8 & 90.2 & 90.4 \\ VGG16 & 18.1 & 18.1 & 25.4 & 18.1 & 18.1 \\ ResNet & 17.7 & 17.7 & 20.8 & 17.7 & 17.7 \\ GoogLeNet & 28.6 & 2.3 & 35.6 & 4.5 & 67.9 \\ LR & 28.9 & 28.9 & 32.6 & 26.4 & 26.3 \\ RF & 79.9 & 79.9 & 92.7 & 79.7 & 79.6 \\ XGB & 78.8 & 78.8 & 92.6 & 78.3 & 77.9 \\ SVM & 36.5 & 33.5 & 42.3 & 32.1 & 31.9 \\ CNN + LR & 32.7 & 32.7 & 45.3 & 31.3 & 30.9 \\ CNN + RF & 80.0 & 80.0 & 93.4 & 79.7 & 79.6 \\ CNN + XGB & 78.8 & 78.8 & 94.4 & 78.3 & 77.9 \\ CNN + SVM & 36.2 & 36.2 & 43.3 & 34.8 & 34.5 \\ SGANs & 43.5 & 14.9 & 75.1 & 23.6 & 72.9 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Results of the LOS prediction using different models for diabetic patients. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & **Accuracy** & **Sensitivity** & **Specificity** & **F-Measure** & **Precision** \\  & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** \\ \hline GAOCNN & 97.2 & 96.7 & 99.3 & 96.9 & 97.1 \\ VGG16 & 38.0 & 38.2 & 37.8 & 45.6 & 38.2 \\ ResNet & 38.0 & 38.2 & 38 & 44.2 & 38.2 \\ GoogLeNet & 39.6 & 38.4 & 50.3 & 38.4 & 38.4 \\ LR & 86.8 & 86.8 & 93.4 & 86.8 & 86.8 \\ RF & 90.0 & 94.4 & 96.5 & 90.0 & 90.0 \\ XGB & 94.4 & 94.4 & 97.8 & 94.4 & 94.5 \\ SVM & 94.9 & 94.3 & 98.4 & 94.9 & 94.9 \\ CNN + LR & 87.5 & 86.5 & 94.2 & 87.5 & 87.4 \\ CNN + RF & 91.7 & 91.4 & 96.8 & 91.7 & 91.7 \\ CNN + XGB & 94.8 & 94.6 & 98.9 & 94.8 & 94.8 \\ CNN + SVM & 95.1 & 95.1 & 95.1 & 95.1 & 95.1 \\ SGANs & 58.9 & 51.7 & 52.6 & 56.9 & 63.3 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Results of readmission prediction for diabetics patients.