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An Artificial Neural Network Classification of Prescription Nonadherence

Author

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  • Steven Walczak

    (School of Information & Florida Center for Cybersecurity, University of South Florida, Tampa, FL, USA)

  • Senanu R. Okuboyejo

    (Covenant University, Ota, Nigeria)

Abstract

This study investigates the use of artificial neural networks (ANNs) to classify reasons for medication nonadherence. A survey method is used to collect individual reasons for nonadherence to treatment plans. Seven reasons for nonadherence are identified from the survey. ANNs using backpropagation learning are trained and validated to produce a nonadherence classification model. Most patients identified multiple reasons for nonadherence. The ANN models were able to accurately predict almost 63 percent of the reasons identified for each patient. After removal of two highly common nonadherence reasons, new ANN models are able to identify 73 percent of the remaining nonadherence reasons. ANN models of nonadherence are validated as a reliable medical informatics tool for assisting healthcare providers in identifying the most likely reasons for treatment nonadherence. Physicians may use the identified nonadherence reasons to help overcome the causes of nonadherence for each patient.

Suggested Citation

  • Steven Walczak & Senanu R. Okuboyejo, 2017. "An Artificial Neural Network Classification of Prescription Nonadherence," International Journal of Healthcare Information Systems and Informatics (IJHISI), IGI Global, vol. 12(1), pages 1-13, January.
    • Handle: RePEc:igg:jhisi0:v:12:y:2017:i:1:p:1-13
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    Cited by:

    1. Yazan Alnsour & Rassule Hadidi & Neetu Singh, 2019. "Using Data Analytics to Predict Hospital Mortality in Sepsis Patients," International Journal of Healthcare Information Systems and Informatics (IJHISI), IGI Global, vol. 14(3), pages 40-57, July.

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