# Hospital Length-of-Stay Prediction Using Machine Learning Algorithms--A Literature Review
## 4 Materials and Methods
### Analysis of Selected Papers
This research demonstrates the practical application of data mining techniques in healthcare, particularly in enhancing operational efficiency and patient management in Neonatal Intensive Care Units. By leveraging ML, this study provides a valuable tool for predicting hospital LoS, thereby potentially helping in resource allocation and potentially reducing healthcare costs. Overall, this research significantly contributes to the field of predictive analytics in healthcare, offering insights that could lead to better patient outcomes and a more efficient use of medical resources. The study in [32] focuses on predicting LoSs in hospitals, which is crucial for optimizing staffing and bed management. Utilizing Electronic Health Records available at admission, including patient demographics, diagnoses, and physiological test results, the study focuses on developing a predictive model applicable to a broad patient spectrum. Despite challenges like data entry delays and absent non-medical factors in electronic health records, the authors designed a data cleaning and integration process to construct a comprehensive dataset for predictive modeling. Through survival analysis, including semi-parametric and parametric models, they achieved similar performances across models, with a concordance index of approximately 0.7. The findings suggest that patient information in electronic health records is sufficient for hospital LoS predictions, showing potential for routine predictions without additional data inputs. Future steps involve real-time data entry facilitation, automation of coding processes, and incorporation of social factors influencing discharge times. Overall, this study underscores the feasibility of leveraging electronic health record data for hospital LoS predictions, offering insights into enhancing hospital operations and resource management. Research paper [33] introduces an ML-based solution to tackle the issue of patient overcrowding in hospital emergency departments. By utilizing decision trees algorithm and training on datasets presenting symptoms, diseases, severity conditions, and admission statuses, the model aims to predict the likelihood of patient admissions to emergency department wards. Through two main phases--disease prediction and admission status prediction--the model helps forecast the number of patients likely to require an admission, enabling hospitals to make necessary resource arrangements in advance. Experimental results indicate an overall accuracy of over 80%, highlighting the effectiveness of the model in aiding resource allocation and mitigating patient overcrowding in emergency departments. In conclusion, the proposed ML model offers a promising approach to enhance emergency department management and patient care. Future directions include the further refinement of the model with additional features, such as predicting diseases admissions, \begin{table}
\begin{tabular}{c c c} \hline \hline
**Study** & **Dataset** & **Size and Features** \\ \hline
[28] & MIMIC—II & 4927 patients and 24 features \\
[29] & UPT Puskesmas Arjasa Kangean & 3055 patient records, 30 features \\
[30] & MIMIC-III & 8024 patient stays, various features \\
[32] & Electronic Health Records & Not specified, various features \\
[33] & MIT MIMIC-III & Not available, various features \\
[34] & Unspecified & 350,393 records, 46 features \\
[35] & Medical Institution Dataset & Not specified, various features \\
[36] & Electronic Health Records & Not specified, 10 features \\
[37] & Femur Fracture Data & 547 (pre-DTAP), 562 (post-DTAP), Various \\  & features \\
[38] & Total Hip Arthroplasty & 792 samples, 13 features \\
[39] & Patient Admission Data & Not specified, various features \\
[40] & Neurosurgical Treatment Cases & 90,685 cases, 6 features \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Dataset overview. and exploring alternative algorithms to enhance performance and address evolving healthcare challenges effectively.