Prediction of Elective Patients and Length of Stay in Hospital

Muhammad Shahzad Saleem; Saif Ullah

Authors

Muhammad Shahzad Saleem UET Taxila, Punjab, Pakistan
Saif Ullah UET Taxila, Punjab, Pakistan

Keywords:

Hospital Management, Patient Stay Duration, Patient Location, Machine Learning Algorithms

Abstract

The efficient management of hospital resources and the optimization of patient care are critical tasks in healthcare systems worldwide. One of the key challenges in hospital management is predicting the duration of a patient's stay and accurately determining their location within the hospital, such as whether they are in the Intensive Care Unit (ICU) or the Operating Theater (OT). In this study, we address this problem statement by employing machine learning algorithms to predict both the stay duration and the location of patients within the hospital. The methods applied in this study include Random Forest, Support Vector Machine (SVM), and K-nearest neighbors (KNN) algorithms. These algorithms utilize patient demographic information such as age, weight, and severity of disease as features to predict the stay duration and location. The dataset used for this study consists of a revised dataset containing relevant patient information. Upon applying the machine learning algorithms, we obtained promising results. The Random Forest algorithm achieved the highest accuracy of 88.6% in predicting patient locations, followed by SVM with an accuracy of 60.8% and KNN with an accuracy of 58.1%. Additionally, Random Forest exhibited superior precision, recall, and F1-scores for both ICU and OT classifications compared to SVM and KNN. The results obtained from this study have several practical implications and potential uses. Firstly, accurate predictions of patient stay duration and location can aid hospital administrators in resource allocation and planning, enabling them to efficiently manage bed occupancy and staffing levels. Additionally, healthcare providers can use these predictions to anticipate patient needs and allocate resources accordingly, thereby enhancing patient care and satisfaction. Moreover, the machine learning algorithms utilized in this study can be integrated into hospital information systems to automate the prediction process, providing real-time insights to healthcare professionals. In conclusion, the application of machine learning algorithms in predicting patient stay duration and location within the hospital offers promising results and valuable insights for hospital management. By leveraging patient demographic information and advanced predictive models, healthcare institutions can improve operational efficiency, enhance patient care delivery, and ultimately optimize resource utilization.

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