Length of Stay Prediction Analysis for Pulmonary Infection Patients using Classification Algorithms

Abstract

This study investigates the relationship between age, gender, and other factors in attributes with Length of Stay (LOS) in patients with pulmonary disease. The main objective of the study was to help predict the LOS of new patients presenting with the same diagnosis and to help reduce the cost of care related to the duration of hospital stay. The theory used in this study is that the factors of age, gender, diagnosis, leukocyte values and chest X-ray results can affect the duration of their stay in the hospital. Data for this study was obtained from the medical records of one of the hospitals in West Java during the study period for approximately 3 months. The methods and techniques used are Artificial Neural Network-MLP (ANN), naïve bayes, J48 and Random Tree to analyze and model the relationship between input variables (age, gender, secondary diagnoses and others) and output variables (LOS). The results of this study are expected to provide a better understanding of the factors that influence LOS in patients with pulmonary diseases, as well as contribute to the development of prediction methods that can help better patient management and clinical decision-making in hospitals.