# Hospital Length-of-Stay Prediction Using Machine Learning Algorithms--A Literature Review
## 6 Discussion
### Critical Analysis  
While performance research emphasizes robust candidates, such as neural networks and XGBoost, an in-depth analysis has revealed context-dependent challenges and limitations. Neural networks excel in accurate predictions, as seen in [29], but require significant data preprocessing and parameter tuning to achieve an optimal performance. The heavy reliance on hyperparameter optimization (e.g., grid search in [29]) means that the performance can vary significantly between systems and datasets if tuning is not performed properly.  
Similarly, XGBoost showed robust results (R\({}^{2}\) = 0.89 in [38]), but it is also computationally demanding and prone to overfitting on small datasets, making them valuable in healthcare contexts, where significant results are rare but can be surprising. Differences in study design and data collection characteristics between studies also influence the reported effectiveness of the algorithms. For example, the neural network underperformed in [34], showing a worse performance compared to random forest, possibly due to small feature sets. In contrast, the neural network in [29] achieved higher accuracy, with more features and broader tuning. This discrepancy highlights the importance of careful selection and validation to ensure that performance metrics reliably reflect the capabilities of the algorithm.  
The complexity of neural network and ensemble models may make them less practical for immediate use in clinical settings. Although neural networks provide high accuracy (as previously mentioned in [29]), their \"black-box\" nature may challenge interpretations, which is a key factor in healthcare decision making. Practical implementations require improvements in interpretability or alternative methods, especially considering that healthcare environments require confidentiality and security. In addition, advanced methods, such as neural networks and XGBoost, have high computational requirements, which may not be feasible in some healthcare systems due to resource limitations.  
Considering the flexibility and consistently high performance of the neural network algorithm in many of the selected studies, it appears to be a promising alternative for hospital LoS predictions. Its flexibility to handle different features allows it to perform well on different datasets, as supported by references [29; 36]. However, it is critical to acknowledge that this success depends on extensive preprocessing and hyperparameter tuning, which may limit its usefulness in settings lacking technical expertise or computational resources.  
Based on critical comparisons and analyses, neural networks exhibit strong adaptive capabilities and predictive capabilities for hospital LoS predictions, but successful applications depend on dataset quality and context-specific needs. They shows great promise  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Study** & **Best Algorithm** & **Metric** & **Values** \\ \hline
[28] & Regression & Ridge Regression & 0.82131 \\ Grid Search Optimization and & Accuracy & 94.74\% \\ Random Search Optimization & & R\({}^{2}\) Score & 0.7780 \\ Random Forest & & R\({}^{2}\) Score & 0.7780 \\ Survival Analysis & & Concordance Index & 0.7 \\ (Various models) & & & Above 80\% \\ Decision Tree & Accuracy & 59.78\% and 36.57\% \\ (35) & Gradient Boosting & Accuracy & 82.41\% \\ (36) & Neural Network & F1 Score & 0.67248 \\ \hline
[37] & Multiple Linear Regression & & \\ (Pre-DTAP) & & & \\ \hline
[38] & XGBoost & R\({}^{2}\) Score & 0.89 \\ \hline
[39] & Support Vector Machine & Accuracy & Not Specified \\ \hline
[40] & GPT-3 & MAE & 2.37 days \\ \hline \hline \end{tabular}
\end{table}
Table 4: Best-performing algorithms.  
for improving patient care and predicting hospital LoSs, especially if the features are appropriately selected and the dataset is well preprocessed.