Patient Discharge Classification Using Machine Learning Techniques

Anthony Gramaje (), Fadi Thabtah (), Neda Abdelhamid () and Sayan Kumar Ray ()
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Anthony Gramaje: Manukau Campus & Manukau Train Station Davies Ave, Manukau
Fadi Thabtah: Manukau Campus & Manukau Train Station Davies Ave, Manukau
Neda Abdelhamid: Auckland Institute of Studies
Sayan Kumar Ray: Manukau Campus & Manukau Train Station Davies Ave, Manukau

Annals of Data Science, 2021, vol. 8, issue 4, No 5, 755-767

Abstract: Abstract Patient discharge is one of the critical processes for medical providers from any health facility to transfer the care of the patient to another care provider after hospitalisation. The discharge plan, final clinical and physical checks, patient education, patient readiness, and general practitioner appointments play an important role in the success of this procedure. However, it has loopholes that need to be addressed to lessen the complexity of managing this critical process. When this is left unchecked, serious consequences and challenges may occur such as re-hospitalisation and financial pressure. This research investigates machine learning technology on the problem of patient discharge by using a real dataset. In particular, the applicability of techniques including Decision Trees, Bayes Net, and Random Forest have been investigated in order to predict the discharge outcome of a patient after surgery. The results of the analysis show that Bayes Net performed better than Decision Tree, and Random Forest in predicting the response variable (class) using tenfold cross validation with respect to classification accuracy. The target audiences of this research are the staff working in a healthcare facility such as clinicians, chief medical officer, and physicians among others.

Keywords: Bayes Net; Data analytics; Data processing; Hospitalisation; Machine learning; Patient discharge; Random Forest (search for similar items in EconPapers)
Date: 2021
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DOI: 10.1007/s40745-019-00223-6

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