Performance Evaluation of Machine Learning Models For Cervical Cancer Prediction
Downloads
Cervical cancer is exclusively an anatomy of the female genitals involving the cervix and is the common cancer type that appears in all age women groups and the most common cause of death associated with cancer in gynecological practice, yet it is almost completely preventable if precancerous lesions are identified and treated promptly. The need to develop a quick, cheap and efficient method to diagnose a precursor lesion in an environment with high burden of the diseases with a view of reducing the burden of the disease motivated the need to apply Machine Learning (ML) technique towards cancer prediction. The primary objective of the study was to develop a ML model that can predict the occurrence of cervical cancer with a higher degree of accuracy. The cervical cancer dataset used in this study was obtained from Jos University Teaching Hospital (JUTH) and Aids Prevention Initiative in Nigeria (APIN). Several ML techniques were considered which includes Ensemble Bagged Tree, Fine Gaussian SVM, Cubic SVM, Fine Tree, Quadratic SVM, Medium Gaussian SVM, Ensemble Boosted Tree, Ensemble Rusboosted Tree, Medium Tree, Linear SVM, Corase Gaussian SVM and Coarse Tree algorithm. The study shows that Ensemble Bagged Tree and Fine Gaussian SVM gives a higher cervical cancer predictive accuracy of 99.7 percent and 99.6 percent respectively as the best performing predictive models, followed by Cubic SVM and Fine Tree with 98 percent and Fine Tree with 96.6 percent cervical cancer predictive accuracy respectively. The performance evaluation shows that Ensemble Bagged Tree and Fine Gaussian SVM perform excellently well in distinguishing and predicting the cervical classes correctly with the best prediction accuracy.
Abdullah, A. A., Abu-Sabri, N. K., Khairunizam, W., Zunaidi, I., Razlan, Z. M., and Shahriman, A. B. (2019). Development of predictive models for cervical cancer based on gene expression profiling data. IOP Conf. Series: Materials Science and Engineering 557: 1 – 8. doi:10.1088/1757-899X/557/1/012003.
Alam, T. M., Khan, M. M. A., Iqbal, M. A. Wahab, A. and Mushtaq, M. (2019). Cervical Cancer Prediction through Different Screening Methods using Data Mining. International Journal of Advanced Computer Science and Applications (IJACSA), Vol. 10, No. 2: 388 – 396.
Aličković, F and Subasi, A. (2017). “Breast cancer diagnosis using GA feature selection and Rotation Forest.” Neural Computing and Applications, Vol. 28: 753 – 763.
Alsmariy, R., Healy, G. and Abdelhafez, H. (2020). Predicting Cervical Cancer using Machine Learning Methods, International Journal of Advanced Computer Science and Applications, (IJACSA), Vol. 11, No. 7, p. 173 – 183.
Al-Wesabi, Y. M. S., Choudhury, A. and Won, D. (2018). Classification of Cervical Cancer Dataset. Proceedings of the 2018 IISE Annual Conference, Binghamton University, USA, p. 1 – 6.
Asadi, F., Salehnasab, C. and Ajori, L. (2020) Supervised Algorithms of Machine Learning for the Prediction of Cervical Cancer. Journal of Biomedical and Physical Engineering. 10(4):513-522. doi: 10.31661/jbpe.v0i0.1912-1027.
Bazazeh, D., and Shubair, R. (2016). Comparative Study of Machine Learning Algorithms for Breast Cancer Detection and Diagnosis, p. 2–5.
Birbrair, A., Zhang, T., Wang, Z. M., Messi, M. L., Olson, J. D., Mintz, A. and Delbono, O. (2014). Type-2 pericytes participate in normal and tumoral angiogenesis. American Journal of Physiology & Cell Physiology, 307 (1): 25-38.
Bishop, C. M. (2006). Pattern recognition and machine learning. New York: Springer.
Bodkhe, A. (2017), “Predicting Pancreatic Cancer Using Support Vector Machine" (2017). Master's Projects. 535. DOI: https://doi.org/10.31979/etd.9w5j-j4ax. Available from https://scholarworks.sjsu.edu/etd_projects/535, accessed: 20/02/2021.
Bora, K., Chowdhury, M., Mahanta, L. B., Kundu, M. K. and Das, A. K. (2016). “Pap Smear Image Classification Using Convolutional Neural Network”, Tenth Indian Conference on Computer Vision, Graphics and Image Processing.
Cancer Council (2019). Understanding Cervical Cancer: A Guide for People with Cancer, Their Families and Friends, Cancer Information: 4 – 68.
Cruz, J. A. and Wishart, D. S. (2006). Applications of Machine Learning in Cancer Prediction and Prognosis, Cancer Informatics, 2: 59– 78.
Dawngliani, M. S., Chandrasekaran, N., Lalmuanawma, S. and Tangkhanhau, H. (2020). Prediction of Breast Cancer Recurrence Using Ensemble Machine Learning Classifiers. Springer Nature Switzerland AG, p. 232 – 243. http://doi.org/10.1007/978-3-030-46828-620.
Kourou, K., Exarchos, T. P., Exarchos, K. P., Karamouzis, M. V. and Fotiadis, D. I. (2015). Machine learning applications in cancer prognosis and prediction, Computational and Structural Biotechnology Journal, 13: 8–17.
Majnik, M. and Bosnic, Z. (2013). ROC Analysis of Classifiers in Machine Learning: A Survey. Intelligent Data Analysis. DOI: 10.3233/IDA-130592.
Mirzajani1, S. S. and Salimi, S. (2018). Prediction and Diagnosis of Diabetes by Using Data Mining Techniques. Avicenna Journal of Medical Biochemistry. Vol. 6 (1), p. 3 – 7. doi:10.15171/ajmb.2018.02/.
Niknejad, A and Petrovic, D. (2013). Introduction to computational intelligence techniques and areas of their applications in medicine. Med Appl Artif Intell.: 51.
Park, K., Ali, A., Kim, D., An, Y., Kim, M. and Shin, H. (2013). Robust predictive model for evaluating breast cancer survivability. Engl. Applied Artificial Intelligence, 26: 2194–205.
Siegel, R. L., Miller, K. D. and Jemal, A. (2017). “Cancer statistics, 2017,” CA: A cancer Journal for Clinicians, vol. 67: 7 – 30.
Singh, J. and Sharma, S. (2019). Prediction of Cervical Cancer Using Machine Learning Techniques, International Journal of Applied Engineering Research, Vol. 14, No. 11, p. 2570 –2577. ISSN 0973-4562.
Teodorescu, M. H. (2017). Machine Learning Methods for Strategy Research. Harvard Business School, HBS Working Paper 18‐011: 1 – 17.
Ude, A. A. (2019). Classification of Breast Cancer using Logistic Regression A Thesis Presented to the Department of Computer Science. M.Sc. Thesis, Department of Computer Science, African University of Science and Technology, p. 10 – 30.
World Health Organization (2006), Comprehensive Cervical Cancer Control A guide to essential practice.
World health organization (2018) Cancer Fact Sheet, February 2018. Retrieved 21 March 2018. https://en.m.wikipedia.org/wiki/Cancer#cite_ref-WHO2018_2-7. Accessed: 11/11/2018.
Yang, S. and Berdine, G. (2017). The Receiver Operating Characteristic (ROC) Curve. The Southwest Respiratory and Critical Care Chronicles, 5(19), p. 34 – 36.
Zhang, L., Lu, L., Nogues, I., Summers, R. M. and Liu, S. (2018). DeepPap: Deep Convolutional Networks for Cervical Cell Classification, arXiv:1801.08616v.
All Content should be original and unpublished.