Volume 5, Issue 2, December 2020, Page: 27-36
A Model for Prediction of Kidney Cancer Using Data Analytics Technique
Aranuwa Felix Ola, Department of Computer Science, Adekunle Ajasin University, Akungba-Akoko, Nigeria
Received: Aug. 31, 2020;       Accepted: Oct. 6, 2020;       Published: Oct. 17, 2020
DOI: 10.11648/j.ajdmkd.20200502.12      View  59      Downloads  27
Across the globe, kidney cancer and other cancerous diseases has been a threat to human lives. The incidence and mortality rate represent a significant and growing threat to both developed and developing countries especially in Africa, where most cancers are diagnosed at an advanced stage. This typically contributes to its complications and high rate of mortality, and has been attributed to limited awareness of early signs and symptoms of the disease, lack of detective mechanism and inaccessible cancer care in our health care centres. To preclude the harm and mortality caused by the disease, an intelligent mechanism for early prediction and prognosis of the syndrome is vital. However, early detection and prognosis requires an accurate information and analytic procedure that will assist and equip the health-care providers/public with the skills to identify early the indicators of the disease. Efforts in this work, produced a model for early prediction of kidney cancer using data analytic approach. Dataset and reports pertaining to the disease were acquired from selected private and public hospitals in fifty-two (52) selected LGA in Nigeria. A two-layered classifier system consisting of Artificial Neural Networks (ANN) and Decision Tree (DT) designed for the work was successfully employed in the model building. Waikato Environment for Knowledge Analysis (WEKA) platform was used for the experiment. The performance of the classifiers considered was compared using standard metrics of accuracy and time taken as benchmark. Experimental results show that the J48 decision tree algorithm outperform all other algorithms in the classifier family with correctly classified instances of 74.7%, F-Measure of 0.614, TP rate of 0.747, FP rate of 0.135, precision and recall of 0.687 and 0.714 respectively. It took the algorithm, 0.03 seconds to build the model. The performance of this algorithm proved its suitability as a valuable tool for the research purpose. The model will in no small measure support the efforts of the national health scheme in preventing the disease mortality rate.
Data Analytics, Kidney Cancer, Classifier System, Algorithm, GLOBOCAN
To cite this article
Aranuwa Felix Ola, A Model for Prediction of Kidney Cancer Using Data Analytics Technique, American Journal of Data Mining and Knowledge Discovery. Vol. 5, No. 2, 2020, pp. 27-36. doi: 10.11648/j.ajdmkd.20200502.12
Copyright © 2020 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Bray, F, Ferlay, J, Soerjomataram, I, Siegel, R. L, Torre, L. A and Jemal, A (2018). Global Cancer Statistics 2018: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. C A Cancer Journal Clinical Oncology. 68 (6): 394-424. doi: 10.3322/caac.21492. Epub 2018 Sep 12. PMID: 30207593.
Ferlay, J. Colombet, M. Soerjomataram, I. Mathers, C. Parkin, D. M. Piñeros, M. Znaor A. and Bray, F. (2019). Estimating the global cancer incidence and mortality. International Journal of Cancer. 144 (1), 1941–1953.
IARC-WHO (2018). Latest global cancer data: Press Release of 12th September, 2018. https://www.who.int/cancer/PRGlobocanFinal.pdf?ua=1.
The American Society of Clinical Oncology (ASCO)(2019). Kidney Cancer. Retrived on 19/08/2020 from https://www.cancer.net/cancer-types/kidney-cancer/introduction.
American Institute for Cancer Research (2018). Kidney cancer Statistics. Retrieved August 2020 from https://www.wcrf.org/dietandcancer/cancer-trends/kidney-cancer-statistics.
WebMD (2017). Understanding Kidney Cancer retrieved on 24/08/2020 from https://www.webmd.com/cancer/understanding-kidney-cancer#1.
Lasebikan, O. A, Nwadinigwe, C. U and Onyegbule, E. C (2014). Pattern of bone tumours seen in a regional orthopaedic hospital in Nigeria. Niger J Med. 2014 Jan-Mar; 23 (1): 46-50.
Kushi LH, Doyle C, McCullough M, et al. (2012). American Cancer Society Guidelines on nutrition and physical activity for cancer prevention.
Nigerian National Systems of Cancer Registries (NSCR), (2014). Newsletter of the Nigerian National Systems of Cancer Registries- 2014.
Jemal, A, Bray, F., Forman, D., O'Brien, M., Center, M., Parkin, D. M (2012). Cancer burden in Africa and opportunities for prevention. American Cancer Society 118 (18): 4372-84. DOI: 10.1002/cncr.27410.
Michael, C. H (2014). Fact Sheet on Kidney Cancer, Cancer Association of South Africa (CANSA) Cansa. Avaliable online at: http://www.cansa.org.za/files/2014/06/Fact-Sheet-Kidney-Cancer-June-2014.pdf.
American Cancer Society, (2014). What is kidney cancer? Retrieved 24/8/2020 from https://www.cancer.org/cancer/kidney-cancer/about/what-is-kidney-cancer.html.
Centre for Disease Control and Prevention CDC (2020). Cancer Statistics Data Visualizations Tool www.cdc.gov/cancer/dataviz, released in June 2020.
UCLA, (2011). University of California, Los Angeles Kidney Cancer Program. https://www.uclahealth.org/urology/kidney-cancer-program.
Xia, B. S., & Gong, P. (2015). Review of business intelligence through data analysis. Benchmarking, 21 (2), 300-311. doi: 10.1108/BIJ-08-2012-0050.
Kohavi, R., Rothleder, N. J’, & Simoudis, A. P (2002). Emerging Trends in Business Analytics Published by ACM Volume 45 Issue 8, Pages 45-48 August 2002.
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