# Hospital Length-of-Stay Prediction Using Machine Learning Algorithms--A Literature Review
## 4 Materials and Methods
### Analysis of Selected Papers
In [34], the short research paper outlines an ML approach to predict patient consultation length in emergency departments, crucial for anticipating operational bottlenecks and improving service provision. With emergency departments facing challenges like increased patient volumes and resource shortages, accurately estimating consultation times becomes essential to mitigate overcrowding and waiting times. Leveraging factors, such as patient demographics, vital signs, arrival time, and triage severity levels, the study proposes two ML models, random forest and neural network, to forecast consultation lengths. By training on a dataset spanning nearly a decade with over 350,000 entries, the models demonstrate improved accuracy when incorporating additional features beyond triage levels, such as patient symptoms, age, doctor's ID, and registration details. Although this research does not perform hospital LoS predictions and focuses on predicting consultation lengths, it is considered important because of the most-used ML algorithms in healthcare subjects and matters. In [35], the authors showed a significant advancement in healthcare analytics, particularly in emergency department management. By harnessing the power of ML algorithms, like logistic regression, modified random forest, and gradient boosting, the study demonstrates the potential to accurately predict whether a patient will require an inpatient admission at the time of the triage. This predictive capability has far-reaching implications, enabling healthcare providers to optimize resource allocation, streamline patient flow, and ultimately enhance the quality of care delivered in emergency settings. Moreover, the comparison of different ML algorithms provides valuable insights into their relative performance in predicting hospital admissions. The superior performance of the gradient boosting algorithm underscores its effectiveness in handling complex medical data and extracting meaningful patterns. In [36], the critical task of predicting whether patients require inpatient or outpatient treatment is addressed, particularly focusing on hospital intensive patients. By leveraging various ML techniques, such as logistic regression, decision trees, random forest, and gradient boosting, and, notably, neural network, the research aims to identify the most accurate predictive model. The methodology includes three key stages: data collection, data preprocessing, and data modeling. Through the utilization of the Electronic Health Record Predicting data obtained from private hospitals in Indonesia, this study examines features such as laboratory test results, patient age, and gender to make informed predictions regarding hospitalization decisions. Among the ML algorithms evaluated, the neural network emerges as the most effective, boasting an accuracy rate of 74.47%. This surpasses the performance of other algorithms, like logistic regression, decision trees, random forest, and gradient boosting. This study contributes valuable insights into the application of ML in healthcare decision making, paving the way for further advancements in predictive analytics and patient management strategies. The study in [37] aims to predict the hospital LoS for patients undergoing femur fracture surgery, particularly focusing on the implementation of a new diagnostic-therapeutic-assistance pathway. By utilizing multiple linear regression models, the researchers analyzed patient data collected before and after the adoption of the diagnostic-therapeutic-assistance pathway. Their findings revealed promising outcomes, with the multiple linear regression models showing a predictive capability when estimating the hospital LoS based on patient demographics, comorbidities, and pre-operative hospital LoS. The pre-diagnostic-therapeutic-assistance pathway model exhibited a slightly better performance compared to the post-diagnostic-therapeutic-assistance pathway model, indicating the potential impact of the new pathway on hospital LoSs.