# Hospital Length-of-Stay Prediction Using Machine Learning Algorithms--A Literature Review
## 1 Introduction  
This paper offers a comprehensive overview of machine learning (ML) algorithms and their applications in the healthcare industry, with an emphasis on hospital length of stay (LoS) prediction. The main objective of this study is to analyze which ML algorithms are the most effective to build a predictive model capable of predicting hospital LoS.  
Initially, different types of ML algorithms are explored, detailing their characteristics and the importance of the most relevant features used for predicting hospital admissions and hospital LoSs. Afterwards, we analyze how these algorithms are used to improve outcomes and efficiency in healthcare. Ethical considerations, such as patient privacy, data management frameworks, and bias mitigation are important for these types of studies, so they are also addressed. These topics provide an overview of the complexities and responsibilities involved in integrating ML into healthcare, ensuring that technologies are not only effective, but also equitable and respectful of patients' rights.  
Hospital admissions and hospital LoSs are multifaceted challenges that involve a complex interplay of patient health, resource availability, and healthcare policies. Accurately predicting admissions or LoSs is notoriously difficult, yet it holds immense potential for improving healthcare system efficiency, reducing costs, and ultimately improving patient outcomes. This article delves into the intricate world of hospital LoS prediction with the goal of providing readers with a thorough understanding of the subject. Among the myriad issues addressed are inaccurate predictions, data complexity, resource constraints, patient variability, and ethical considerations.  
The contributions offered by this review paper include improved prediction accuracy, enhanced decision support, cost reduction initiatives, and the development of an ethical framework.  
This article focuses on the use of ML algorithms to predict hospital LoS. Therefore, four research questions (RQ) were formulated:  
* RQ1: What are the key factors that significantly influence hospital LoS?
* RQ2: What types of ML algorithms have been employed for hospital LoS predictions?
* RQ3: How do these algorithms compare in terms of performance metrics to predict hospital LoSs?
* RQ4: What are the potential benefits and limitations of using ML for hospital LoS predictions in healthcare settings?  
This paper is a review on the prediction of hospital LoS using ML algorithms. Section 2 starts with a brief overview of ML algorithms and their use in healthcare systems, followed by their use for hospital LoS predictions. Section 3 describes the ethical considerations we must pay attention to when using these types of data, and also when using ML algorithms. Section 4 describes the [PERSON]'s methodology used and the work developed using it, up to an analysis of twelve selected research articles relevant to the topic using this methodology. It includes a detailed outline of the research protocol, covering the data source, search string, prerequisites, and inclusion and exclusion criteria. Section 5 presents the results provided by each study. Section 6 provides an analysis and discussion of the results. Finally, Section 7 concludes the article by summarizing and highlighting the potential of leveraging ML applied to hospital LoS prediction, ultimately promoting improved healthcare delivery. Through this review, the paper aims to provide readers with a comprehensive understanding of the potential advantages and challenges associated with hospital LoS predictions using ML algorithms.