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


  •   Olatunbosun Olabode

  •   Adebayo O. Adetunmbi

  •   Folake Akinbohun

  •   Ambrose Akinbohun


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.

Keywords: Decision Tree, Head and Neck Cancer, k-NN, Nasopharyngeal


Altman, N. S. (1992). An Introduction to kernel and kernel-Neighbour non parametric regression. The American Statistician 46(3): 175-185. Doi:10.1080/00031305.1992.10475879

Baatenburg, D R. J., Hermans, J., Molenar, J., Briaire, J. J. and Le Cessie, S. (2001). Prediction of Survival in Patients with Head and Neck Cancer. National Center for Biotechnology Information, John Wiley & Sons, Inc.

Benjamín, M. and Carlos, H. M. (2013). Prediction System of Larynx Cancer. The Fourth International Conference on Computational Logics, Algebras, Programming, Tools, and Benchmarking

Durairaj, M. and Deepika R. (2015). Prediction of Acute Myeloid Leukemia Cancer using Data Mining- A Survey. International Journal of Emerging Technology and Innovative Engineering 1(2)

Erinoso, O. A., Okoturo, E., Gbotolorun, O. M., Effiom, O. A., Awolola, N. A., Soyemi, S. S. and Oluwakuyide, R. T. (2016). Emerging Trends in the Epidemiological Pattern of Head and Neck Cancers in Lagos, Nigeria. Annals Medical Health Science Res. 6(5): 301–307

GBD (2015). Mortality and Causes of Death, Collaborators. Global, Regional, and National Life Expectancy, All-Cause Mortality, and Cause-Specific Mortality for 249 Causes of Death, 1980-2015: A Systematic Analysis for the Global Burden of Disease Study 2015. Lancet. 388 (10053): 1459–1544.

Hagedoorn, M. and Molleman, E. (2006). Facial Disfigurement in Patients with Head and Neck Cancer: The Role of Social Self-Efficacy. American Psychological Association. 25(5): 643–647

Hussein, A., Shigeo, K. and Yasuhiro, A. (2002). Development and Applications of Decision Trees. Expert Systems. 1(1):53-77

Jacqueline, A. E., L., Jos T., Martine, K., Patricia, D., Mark, H.H., Kramer, P. M., and Weijs, and Leemans, C. R. (2015). Prediction Model to Predict Critical Weight Loss in Patients with Head and Neck Cancer during (Chemo) Radiotherapy. Oral Oncology, 52(1): 91-96

Jiawei H., Micheline, K. and Jian, P. (2011). Data Mining: Concepts and Techniques 3rd Edition

Han, J., Kamber, M. and Pei, J. (2012). Data Mining: Concepts and Techniques, 3rd Edition. Elsevier, Amsterdam

John, C. W., Mark, N. G., and Janet A. W. (2000). Stell and Maran’s Head and Neck Surgery. Butterworth Heinemann. Fourth Edition

Opubo, B. D., Abayomi, O. S. and Wasiu, L. A. (2009). Current Evidence on the Burden of Head and Neck Cancers in Nigeria. Head Neck Oncol.

Rajeswari, B. and Aruchamy, R. (2014). Survey on Data Mining Algorithms to predict Leukemia Types International Journal for Research Science Engineering and Technology. (IJRSET) 2(5): 42-46

Sami, P. M., John, S. S., Tareck, A., Louis, G., Eric, B., Olguta, E. G., Denis, S., Louise, L., Edith, F., Phuc, F. N., and Apostolos, C. (2015). Predicting Depression and Quality of Life among Long-Term Head and Neck Cancer Survivors. American Academy of Otolaryngology—Head and Neck Surgery. 152(1): 91–97

Shakhnarovish, D. and Indyk (2005). Nearest-Neighbor Methods in Learning and Vision.


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How to Cite
Olabode, O., Adetunmbi, A., Akinbohun, F. and Akinbohun, A. 2020. Application of Machine Learning for Diagnosis of Head and Neck Cancer in Primary Healthcare Organisation. European Journal of Engineering and Technology Research. 5, 4 (Apr. 2020), 489-493. DOI: