A Patient Care Predictive Model using Logistic Regression

Abstract

Medical treatments and operations in hospitals are divided into in-patient and out-patient procedures. It is critical for patients to know and understand the differentiation between these two forms of treatment since it will affect the time of a patient's stay in a hospital or a medical institution as well as the cost of a treatment. In today's era of information, a person's talents and expertise may be put to good use by automating activities wherever possible. A medical service will be termed inpatient care if a doctor issues an order and the patient is admitted to the hospital on that order whereas a patient seeking outpatient care do not need to spend the night in a hospital. Choosing between in-patient and out-patient care is usually a matter of how involved the doctor wants to be with the patient's treatment. With the aid of numerous data points regarding the patients, their illnesses, and lab tests, our main objective is to develop a system as part of the hospital automation system that predicts and estimates whether the patient should be given an inpatient care or an out-patient care. The main idea of the paper is to understand and develop a logistic regression model to predict whether a patient needs to be treated as an in-patient or an outpatient depending on the results of laboratory tests. Furthermore, this study also focuses on how logistic regression performs for this dataset. In addition, research on how logistic regression performs for this dataset was also not done. From the study, the results show that logistic regression gives an accuracy of 75%, F1-score of 73%, precision of 74% and recall of 74%.