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Patient Discharge Classification Using Machine Learning Techniques

Author

Listed:
  • 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)

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.

Suggested Citation

  • Anthony Gramaje & Fadi Thabtah & Neda Abdelhamid & Sayan Kumar Ray, 2021. "Patient Discharge Classification Using Machine Learning Techniques," Annals of Data Science, Springer, vol. 8(4), pages 755-767, December.
  • Handle: RePEc:spr:aodasc:v:8:y:2021:i:4:d:10.1007_s40745-019-00223-6
    DOI: 10.1007/s40745-019-00223-6
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    References listed on IDEAS

    as
    1. Fadi Thabtah & Qazafi Mahmood & Lee McCluskey & Hussein Abdel-Jaber, 2010. "A New Classification Based on Association Algorithm," Journal of Information & Knowledge Management (JIKM), World Scientific Publishing Co. Pte. Ltd., vol. 9(01), pages 55-64.
    2. Fadi Thabtah & Neda Abdelhamid, 2016. "Deriving Correlated Sets of Website Features for Phishing Detection: A Computational Intelligence Approach," Journal of Information & Knowledge Management (JIKM), World Scientific Publishing Co. Pte. Ltd., vol. 15(04), pages 1-17, December.
    3. Hyunyoung Baek & Minsu Cho & Seok Kim & Hee Hwang & Minseok Song & Sooyoung Yoo, 2018. "Analysis of length of hospital stay using electronic health records: A statistical and data mining approach," PLOS ONE, Public Library of Science, vol. 13(4), pages 1-16, April.
    4. Fadi Thabtah, 2006. "Rule Preference Effect in Associative Classification Mining," Journal of Information & Knowledge Management (JIKM), World Scientific Publishing Co. Pte. Ltd., vol. 5(01), pages 13-20.
    5. Fadi Thabtah & Li Zhang & Neda Abdelhamid, 2019. "NBA Game Result Prediction Using Feature Analysis and Machine Learning," Annals of Data Science, Springer, vol. 6(1), pages 103-116, March.
    6. Paul Town & Fadi Thabtah, 2019. "Data Analytics Tools: A User Perspective," Journal of Information & Knowledge Management (JIKM), World Scientific Publishing Co. Pte. Ltd., vol. 18(01), pages 1-16, March.
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    Cited by:

    1. Mahsa Pahlevani & Majid Taghavi & Peter Vanberkel, 2024. "A systematic literature review of predicting patient discharges using statistical methods and machine learning," Health Care Management Science, Springer, vol. 27(3), pages 458-478, September.

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