Paper Title : EFFECTIVE PREDICTION OF PATIENT ADMISSION IN HOSPITAL USING DATA MINING CLASSIFICATION ANALYTICAL TOOL
ISSN : 2394-2231
Year of Publication : 2021
MLA Style: Ms. N. Zahira Jahan M.C.A., M.Phil.,Ms. K. Priyanka "EFFECTIVE PREDICTION OF PATIENT ADMISSION IN HOSPITAL USING DATA MINING CLASSIFICATION ANALYTICAL TOOL " Volume 8 - Issue 2 March-April , 2021 International Journal of Computer Techniques (IJCT) ,ISSN:2394-2231 , www.ijctjournal.org
APA Style: Ms. N. Zahira Jahan M.C.A., M.Phil.,Ms. K. Priyanka "EFFECTIVE PREDICTION OF PATIENT ADMISSION IN HOSPITAL USING DATA MINING CLASSIFICATION ANALYTICAL TOOL " Volume 8 - Issue 2 March-April , 2021 International Journal of Computer Techniques (IJCT) ,ISSN:2394-2231 , www.ijctjournal.org
In hospitals, managing crowd is a big issue. Particularly, in emergency departments may create major negative consequences for patients. Emergency departments need to explore the use of innovative methods to improve the patient flow and prevent overcrowding. One potential method is the use of data mining using machine learning techniques to predict emergency department admissions. This project uses routinely collected administrative data and to compare contrasting machine learning algorithms in predicting the risk of admission from the emergency department. The existing system draws on this data to achieve two objectives. First one is to make a model that accurately predicts admission to hospital from the emergency department. Second is to measure the performance of common machine learning algorithms in predicting hospital admissions. This project suggests using the cases for the implementation of the model as a decision support and performance management tool. The logistic regression and decision tree models presented in this project yield comparable, and in some cases improved performance compared to models presented in other studies. Implementation of the models as a decision support tool could help hospital decision makers to more effectively plan and manage resources based on the expected patient inflow from the emergency department. This could help to improve patient flow and reduce emergency department crowding, therefore reducing the adverse effects of emergency department crowding and improving patient satisfaction.The models even have potential application in performance monitoring and audit by comparing predicted admissions against actual admissions. However, at the same time as the model could be used to support planning and decision making, individual level admission decisions still require clinical judgments.
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————Data mining, emergency department, hospitals, machine learning, predictive models