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Application of Machine Learning for Diagnosis of Head and Neck Cancer in Primary Healthcare Organisation

Author

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  • Olatunbosun Olabode

    (Computer Science Department, Federal University of Technology, Akure, Ondo State, Nigeria)

  • Adebayo O. Adetunmbi

    (Federal University of Technology, AkurDepartment of Computer Science, Federal University of Technology, Akure, Ondo State, Nigeria e, Ondo State, Nigeria)

  • Folake Akinbohun

    (Department of Computer Science, Rufus Giwa Polytechnic, Owo, Ondo State)

  • Ambrose Akinbohun

    (University of Medical Sciences Teaching Hospital, Akure, Ondo State, Nigeria)

Abstract

Head and neck cancers (HNC) are indicated when cells grow abnormally. The disturbing rate of morbidity and mortality of patients with HNC due to late presentation is on the increase especially in Africa (developing countries). There is need to diagnose head and neck cancer early if patients present so that prompt referral could be facilitated. The collected data consists of 1473 instances with 18 features. The dataset was divided into training and test data. Two supervised learning algorithms were deployed for the study namely: Decision Tree (C4.5) and k-Nearest Neighbors (KNN). It showed that Decision Tree outperformed with accuracy of 91.40% while KNN had accuracy of 88.24%. Hence, machine learning algorithm like Decision Tree can be used for diagnosis of HNC in healthcare organisations.

Suggested Citation

  • Olatunbosun Olabode & Adebayo O. Adetunmbi & Folake Akinbohun & Ambrose Akinbohun, 2020. "Application of Machine Learning for Diagnosis of Head and Neck Cancer in Primary Healthcare Organisation," European Journal of Engineering and Technology Research, European Open Science, vol. 5(4), pages 489-493, April.
    • Handle: RePEc:epw:ejeng0:v:5:y:2020:i:4:id:61886
    DOI: 10.24018/ejeng.2020.5.4.1886
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