# Hospital Length-of-Stay Prediction Using Machine Learning Algorithms--A Literature Review
## 4 Materials and Methods
### Analysis of Selected Papers  
We analyzed 12 articles. These articles focused on ML applied to hospital LoS prediction. ML is a subset of predictive business analytics that can empower healthcare providers to analyze large datasets, helping them to uncover valuable insights useful for the process of making decisions. The 12 articles that were analyzed here were obtain using [PERSON]'s methodology [26], described in Section 3 in this work. In the study carried out in [28], the authors aim to develop a decision support system to assist physicians in predicting hospital LoS using various regression models (Table 2). Utilizing the MIMIC-II dataset (Table 1), the authors compared the performance of several algorithms (Table 2). Their methodology included data preprocessing, such as converting categorical features to numerical values, and normalizing the data. The study found that regularization techniques, particularly ElasticNet, outperform simple linear regression in terms of the mean absolute error (MAE), highlighting the importance of regularization for preventing overfitting and improving model generalizability. The study's strength lies in its practical application, evident motivation, and comprehensive comparative analysis of different regression models. Their research demonstrates practical relevance and usefulness by utilizing real-world datasets. This study makes a significant contribution to the field of healthcare management and predictive analytics by demonstrating the potential of regularization techniques in enhancing prediction accuracy. Article [29] develops a system to predict hospital LoS using neural networks, aiming to enhance efficient hospital resource management in Indonesia. Using a dataset from UPT Puskesmas Arjasa Kangean, the authors analyzed 3055 patient records with 30 attributes, implementing a neural network for prediction. They emphasized the importance of data preprocessing, including nominal to binary transformations, using one-hot encoding, and feature selection with the random forest algorithm. The optimal model configuration, determined through grid search and random search techniques, achieved an accuracy of 94.74% with the following specific parameters: Epoch: 50, hidden units: 52, batch size: 4000, Adam optimizer, and linear activation. The application of the neural network in this study enhances hospital management by predicting hospital LoS accurately, thus aiding in better resource allocation and operational efficiency. However, this study could be improved by expanding on the interpretability of the neural network model, such as analyzing the significance of different features in the prediction. Additionally, testing the model on diverse datasets from other hospitals would enhance its generalizability. Overall, the research provides a valuable contribution to healthcare informatics by showcasing the practical utility of neural networks when predicting patient hospital LoS and improving hospital efficiency through advanced data analytics and visualization techniques. In article [30], the authors explore the use of ML models to predict the hospital LoS for neonates in the Neonatal Intensive Care Unit [31]. Utilizing the MIMIC-III database, which includes data from 8024 Neonatal Intensive Care Unit stays, the study emphasizes the significance of efficient resource allocation in critical care settings. The authors preprocessed the data through feature engineering, including the transformation of categorical variables using one-hot encoding and the imputation of missing values with the Asexual Reproduction Optimization algorithm. The study employs various regression models, namely random forest, XGBoost, k-nearest neighbors, logistic regression, and gradient boosting, ultimately achieving a high R2 score of 0.78 using patient diagnoses and demographic data collected within the first 24 h of admission.