Fadratul Hafinaz Hassan
ABSTRACT
Smoking1 brings the biggest cause of cancer and deaths every year. It is essential for the smoker to aware of the bad effect of smoking and quit smoking. Therefore, cancer prediction tools are used to help in early diagnosis of cancer for the smoker for them to change their lifestyle to lower the risk of getting cancer in the future. Recently, there are many research study on early detection and diagnosis of cancer by using machine learning techniques. The cancer prediction algorithms that discussed here are decision tree algorithm, linear regression algorithm and support vector machine algorithm. These algorithms are widely used in the development of cancer prediction model. The strengths, limitations, and accuracy of each cancer prediction model are compared and analysed. In conclusion, linear regression algorithm shown the best result for the cancer prediction based on the analysis done on each cancer prediction algorithm.
- S. P Saha, D. K Bhalla, T. F Whayne and C. Gairola. 2007. Cigarette smoke and adverse health effects: An overview of research trends and future needs. International Journal of Angiology, vol. 16, no. 03, 77--83.Google Scholar
Cross Ref
- H. Lim, H. Teh, L. Lim, J. Lau, C. Kee, S. Ghazali, Y. Chan, M. Sabtu, H. Ismail, N. Zaki, L. Thomas, K. Lim, S. Cheong, N. Ibrahim and M. Yusoff. 2015. Smoking among Secondary School Students in Kota Tinggi, Johor, Malaysia - Findings from a Cross-Sectional Study. Asian Pacific Journal of Cancer Prevention, vol. 16, no. 11, 4563--4570.Google ScholarCross Ref
- World Health Organization. Early cancer diagnosis saves lives, cuts treatment costs. Updated February 2017. Available online: http://www.who.int/mediacentre/news/releases/2017/early-cancer-costs/en/ (accessed on 1 September 2017).Google Scholar
- W. Loh. 2014. Fifty Years of Classification and Regression Trees. International Statistical Review, vol. 82, no. 3, 329--348.Google ScholarCross Ref
- S. Salzberg. 1994. C4.5: Programs for Machine Learning by J. Ross Quinlan. Morgan Kaufmann Publishers, Inc., Machine Learning, vol. 16, no.3, 235--240. Google ScholarDigital Library
- Han, Jiawei, Kamber, Micheline, 2000. Data Mining: Concepts and Techniques. Morgan Kaufmann. Google ScholarDigital Library
- Kemal Hakan Gülkesen, İsmail Türker Köksal, Sebahat özdem, Osman Saka 2010. Prediction of prostate cancer using decision tree algorithm. Turkish Journal of Medical Sciences, 40(5), 681--686.Google Scholar
- A. Gordon, L. Breiman, J. Friedman, R. Olshen and C. Stone. 1984. Classification and Regression Trees. Biometrics, vol. 40, no. 3, 874.Google ScholarCross Ref
- M. Garzotto, T. Beer, R. Hudson, L. Peters, Y. Hsieh, E. Barrera, T. Klein and M. Mori. 2005. Improved Detection of Prostate Cancer Using Classification and Regression Tree Analysis. Journal of Clinical Oncology, vol. 23, no. 19, 4322--4329.Google ScholarCross Ref
- E. Alexopoulos. 2010. Introduction to Multivariate Regression Analysis, pp. 14(Suppl 1): 23--28.Google Scholar
- Park S, Nam B-H, Yang H-R, Lee JA, Lim H, Han JT, et al. (2013) Individualized Risk Prediction Model for Lung Cancer in Korean Men. PLoS ONE 8(2): e54823.Google ScholarCross Ref
- Hosseinzadeh, F., KayvanJoo, A.H., Ebrahimi, M. et al. SpringerPlus (2013) 2: 238.Google Scholar
- C. Lynch, B. Abdollahi, J. Fuqua, A. de Carlo, J. Bartholomai, R. Balgemann, V. van Berkel and H. Frieboes. 2017. Prediction of lung cancer patient survival via supervised machine learning classification techniques. 2017. International Journal of Medical Informatics, vol. 108, 1--8.Google ScholarCross Ref
- Y. Liu, R. Ni, H. Zhang, L. Miao, J. Wang, W. Jia and Y. Wang. 2016. Identification of feature genes for smoking-related lung adenocarcinoma based on gene expression profile data. OncoTargets and Therapy, vol. 9, 7397--7407.Google ScholarCross Ref
- Huang C, Mezencev R, McDonald JF, and Vannberg F. 2017. Open source machine-learning algorithms for the prediction of optimal cancer drug therapies. PLoS ONE 12(10): e0186906.Google ScholarCross Ref
- Lora A. Plaskon, David F. Penson, Thomas L. Vaughan and Janet L. Stanford. 2003. Cigarette Smoking and Risk of Prostate Cancer in Middle-Aged Men. Cancer Epidemiol Biomarkers Prev, vol. 12, no.2, 604--609.Google Scholar
- V.Krishnaiah, G.Narsimha, and N.Subhash Chandra. 2013. Diagnosis of Lung Cancer Prediction System Using Data Mining Classification Techniques. (IJCSIT) International Journal of Computer Science and Information Technologies, vol. 4, no.3, 39 - 45.Google Scholar
- Hippisley-Cox, J., and Coupland, C. 2015. Development and validation of risk prediction algorithms to estimate future risk of common cancers in men and women: Prospective cohort study. BMJ Open, 5(3)Google Scholar
- Hippisley-Cox, J., Coupland, C., and Brindle, P. 2017. Development and validation of QRISK3 risk prediction algorithms to estimate future risk of cardiovascular disease: Prospective cohort study. BMJ: British Medical Journal (Online).Google Scholar
- Sun T, Zhang R, Wang J, Li X, and Guo X. 2013. Computer-Aided Diagnosis for Early-Stage Lung Cancer Based on Longitudinal and Balanced Data. PLoS ONE 8(5): e63559.Google ScholarCross Ref
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