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Risk Prediction System using Data Mining Techniques in Gynecological Ovarian Cancer
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Cancer is one of the leading causes of death worldwide. Early detection and prevention of cancer plays a very important role in reducing deaths caused by cancer. Ovarian Cancer (OC) is a type of cancer that affects ovaries in women, and is difficult to detect at initial stage due to which it remains as one of the leading causes of cancer death. Identification of genetic and environmental factors is very important in developing novel methods to detect and prevent cancer. This research uses data mining technology such as classification, clustering and prediction to identify potential cancer patients. Therefore a cancer risk prediction system is here proposed which is easy, cost effective and time saving.
Keywords
Ovarian Cancer, Multi-Layer Perceptron Classifier, Detection.
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- B.T. Hennessy, R.L. Coleman and M. Markman, “Ovarian Cancer”, Lancet, Vol. 374, No. 9698, pp. 1371-1382, 2009.
- J.O. Schorge, S.C. Modesitt, R.L. Coleman, D.E. Cohn, N.D. Kauff, L.R. Duska and T.J. Herzog, “SGO White Paper on Ovarian Cancer: Etiology, Screening and Surveillance”, Gynecologic Oncology, Vol. 119, No. 1, pp. 7-17, 2010.
- S. Jothi and S. Anitha, “Data mining Classification Techniques Applied of Cancer Disease-A Case Study using Xlminer”, International Journal of Engineering Research and Technology, Vol. 1, No. 8, pp. 23-32, 2012.
- T. Jayalakshmi and A. Santhakumaran, “A Novel Classification Method for Classification of Diabetes Mellitus using Artificial Neural Networks”, Proceedings of International Conference on Data Storage and Data Engineering, pp. 159-163, 2010.
- P. Ramachandran, N. Girija and T. Bhuvaneswari, “Early Detection and Prevention of Cancer using Data Mining Techniques”, International Journal of Computer Applications, Vol. 97, No. 13, pp. 1-8, 2014.
- B. Rosiline Jeetha and M. Malathi, “Diagnosis of Ovarian Cancer using Artificial Neural Network”, International Journal of Computer Trends and Technology, Vol. 4, No. 10, pp. 3601-3606, 2013.
- J. Hyeon, H.J. Choi, B.D. Lee and K.N. Lee, “Diagnosing Cervical Cell Images using Pre-Trained Convolutional Neural Network as Feature Extractor”, Proceedings of International Conference on Big Data and Smart Computing, pp. 411-417, 2017.
- C.J. Tseng, C.J. Lu, C.C. Chang and G.D. Chen, “Integration of Data Mining Classification Techniques and Ensemble Learning to Identify Risk Factors and Diagnose Ovarian Cancer Recurrence”, Artificial Intelligence in Medicine, Vol. 78, pp. 47-54, 2017.
- Antonia Vlahou, John O. Schorge, Betsy W. Gregory and Robert L. Coleman, “Diagnosis of Ovarian Cancer Using Decision Tree Classification of Mass Spectral Data”, Journal of Biomedicine and Biotechnology, Vol. 5, No. 1, pp. 308-314, 2003.
- P. Yasodha and N.R. Ananthanarayanan, “Detecting the Ovarian Cancer using Big Data Analysis with Effective Model”, Biomedical Research, Vol. 31, No. 1, pp. 1-14, 2018.
- Ahmed Osmanoviu, Layla Abdel Ilah, Adnan Hodziu, Jasmin Kevric and Adnan Fojnica, “Ovary Cancer Detection using Decision Tree Classifiers based on Historical Data of Ovary Cancer Patients”, Proceedings of the International Conference on Medical and Biological Engineering, pp. 344-349, 2017.
- Tuan Zea Tan, Chai Quek, Geok See Ng and Khalil Razvi, “Ovarian Cancer Diagnosis with Complementary Learning Fuzzy Neural Network”, Artificial Intelligence in Medicine, Vol. 43, pp. 207-222, 2008.
- Pooja Agrawal, Suresh Kashyap, Vikas Chandra Pandey and Suraj Prasad Keshri, “Knowledge Patterns in Clinical Data through Data Mining: A Review on Cancer Disease Prediction”, International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering, Vol. 2, No. 4, pp. 56-64, 2013.
- Baolin Wu, Tom Abbott, David Fishman, Walter McMurray, Gil Mor, Kathryn Stone, David Ward, Kenneth Williams and Hongyu Zhao, “Comparison of Statistical Methods for Classification of Ovarian Cancer using Mass Spectrometry Data”, Bioinformatics, Vol. 19, No. 13 pp. 1636-1643, 2003.
- H. Yan, Y. Jiang, J. Zheng, C. Peng and Q. Li, “A Multilayer Perceptron-based Medical Decision Support System for Heart Disease Diagnosis”, Expert Systems with Applications, Vol. 30, No. 2, pp. 272-281, 2006.
- M. Azarbad, S. Hakimi and A. Ebrahimzadeh, “Automatic Recognition of Digital Communication Signal”, International Journal of Energy, Information and Communications, Vol. 3, No. 4, pp. 21-34, 2012.
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