# Hospital Length-of-Stay Prediction Using Machine Learning Algorithms--A Literature Review
## 2 Machine Learning Algorithms in Healthcare
### Application of Machine Learning Algorithms in Healthcare  
Artificial intelligence can be used for so much these days, and one area that can benefit is healthcare [10]. One notable application of ML in healthcare is for the early detection and treatment of sepsis, a life-threatening condition. In a randomized clinical trial, a machine learning-based algorithm predicted severe sepsis with greater accuracy than traditional methods, such as SOFA and SIRS scoring systems. This resulted in significant reductions in both hospital length of stay (20.6%) and in-hospital mortality (58%), highlighting the transformative potential of ML to improve patient outcomes through early intervention [11]. Some of the main applications of ML algorithms in healthcare include:  
* Diagnosis: ML algorithms can be used to analyze data from medical exams, such as images, laboratory tests, and clinical data, with the aim of identifying patterns that may indicate the presence of a disease, for example;
* Personalized medicine: The patient can be analyzed and be administered personalized medicine based on his condition;
* Treatment: ML algorithms can also be used to personalize the treatment of patients based on their individual characteristics and the history of their disease, which will consequently help to improve the effectiveness and safety of the treatment;
* Outbreak prediction: AI and machine learning are used to monitor and predict epidemics by analyzing vast data from satellites, social media, and websites, aiding in disease outbreak predictions, especially in countries in the Global South.  
Despite the revolutionary potential for healthcare, the application of ML algorithms also faces some challenges, such as:  
* Bias is present in ML;
* Lack of quality data;
* Data annotation must be performed accurately;
* Need for hyperparameter tuning.  
One of the main challenges is the need for large datasets to train the algorithms. Also, algorithms that are developed should be validated in an ethical and responsible way. The opportunities offered by ML are significant, where the technology has the potential to improve the quality of medical care, reduce costs, and increase the efficiency of the healthcare system, which decreases the risk relative to the reward.  
Especially when dealing with complex multivariate time-series data, ML algorithms have become increasingly important for predicting hospital LoS, due to algorithms such as random forest and support vector machine. This is due in part to their robustness and processability with high-dimensional data applications in healthcare, where these models are used to predict patient outcomes using various clinical criteria of variables, such as patient demographics and vital signs. Random forest is particularly effective because of its ability to capture complex interactions that require intensive preprocessing due to its invariance. Support vector machine, on the other hand, excels at classification tasks with smaller datasets and can handle outliers more efficiently. A comparative study of these algorithms, as performed by [PERSON] et al. [12], shows versatility and efficiency in a variety of industries, in terms of time-series classification. Their results show that random forest, support vector machine, and other algorithms are still very competitive when applied to time-series data, making them suitable for clinical LoS prediction tasks, where time series and patient changes must be accurately modeled.