# Hospital Readmission and Length-of-Stay Prediction Using an Optimized Hybrid Deep Model
## 5 Discussion  
The GAOCNN uses a hybrid structure of deep 1D convolutional networks with GA, and it is adequate for situations where the existing data is imbalanced and gathering more data is difficult. Notably, applying the proposed model helps develop an expert system to predict hospital readmission and LOS accurately. The GAOCNN is well-tuned for the readmission and the LOS prediction tasks. To evaluate the GAOCNN, we used datasets of diabetic, COVID-19, and ICU patients. The results show that the GAOCNN has a significant accuracy in predicting hospital readmission and LOS compared to existing techniques. The main contribution of this research is to help manage hospitals' resources more accurately. Furthermore, the proposed model applies to various conditions such as chronic diseases, pandemics, and intensive care. It is another contribution of this research proposing one model for different conditions.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Authors** & **Accuracy (\%)** & **AUROC (\%)** \\ \hline [PERSON] and [PERSON] [55] (2017) & 75.9 & - \\ [PERSON] et al. [18] (2018) & 92 & 95 \\ [PERSON] et al. [50] (2018) & 82.27 & - \\ [PERSON] et al. [56] (2019) & 94.8 & - \\ [PERSON] and [PERSON] [57] (2019) & 95 & 95 \\ [PERSON] et al. [58] (2020) & 86 & 66.7 \\ [PERSON] [59] (2021) & 89.7 & 96 \\ GAOCNN & 97.2 & 99 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Comparison of the proposed model with the published works for readmission prediction on the diabetes dataset.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Authors** & **Number of Classes** & **Accuracy (\%)** & **AUROC** & **Dataset** \\ \hline [PERSON] et al. [60] (2017) & 2 & 79.8 & - & MIMIC-III \\ [PERSON] and [PERSON] [61] (2019) & 2 & 87.7 & 88 & Diabetes \\ [PERSON] et al. [56] (2019) & 3 & 85.4 & - & Diabetes \\ [PERSON] et al. [27] (2020) & 2 & 66.2 & 88 & MIMIC-III \\ [PERSON] et al. [25] (2020) & 2 & 68.3 & 73.3 & MIMIC-III \\ [PERSON] et al. [25] (2020) & 2 & 91.2 & 71 & MIMIC-III \\ [PERSON] et al. [62] (2022) & 2 & 85 & 93 & COVID-19 \\ [PERSON] et al. [63] (2022) & 9 & 94.16 & - & COVID-19 \\ GAOCNN & 7 & 89 & 96 & Diabetes \\ GAOCNN & 13 & 94.1 & 99 & MIMIC-III \\ GAOCNN & 9 & 99.4 & 99 & COVID-19 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Comparison of the proposed model and the published works for the LOS prediction on diabetes, COVID-19, and MIMIC-III datasets.  
GAOCNN presents a CNN model for accurate LOS prediction. Thus, we used forward and backward feature selection techniques [64] to specify the most critical features for LOS and readmission time frame classification. The result of feature selection based on accuracy is shown in Figure 10.  
As shown in Figure 10, specific features such as first diagnosis, symptoms, age, and gender are more important than other features for LOS and readmission time frame classification. Using the proposed approach, there is no need to deal with hyperparameters. To achieve a balanced dataset, we considered different numbers of classes for LOS. Then, we combined over and under-sampling methods to decrease the difference between class densities. Considering the high performance of the GAOCNN model, we can develop a system that aids healthcare systems to improve their medical services allocation and apply proper management to staff and patients. To predict the readmission time frame, we have prioritized accuracy over loss, while for predicting the LOS, we have prioritized loss over accuracy.