Predictive Modeling of Patient Length of Hospital Stay Using Advanced Machine Learning Techniques: Develops machine learning models to predict the length of hospital stays for patients, optimizing resource allocation and discharge planning in healthcare s

Abstract

In the healthcare industry, efficient management of resources and timely discharge planning are critical for ensuring optimal patient care and hospital operational efficiency. Predicting the length of hospital stays for patients plays a crucial role in resource allocation and discharge planning. This research paper focuses on the development of machine learning models for predicting the length of hospital stays for patients, aiming to optimize resource allocation and discharge planning in healthcare settings. The study utilizes a dataset of patient records, including demographic information, medical history, and treatment details, to train and evaluate the performance of machine learning models. Various machine learning algorithms, such as linear regression, decision tree, random forest, and gradient boosting, are employed to develop predictive models. The models are evaluated based on performance metrics such as mean absolute error, root mean squared error, and R-squared value. The results demonstrate the effectiveness of machine learning models in predicting the length of hospital stays, providing valuable insights for healthcare providers to optimize resource allocation and improve discharge planning processes.