Ensemble based optimal classification model for pre-diagnosis of lung cancer

2013 4th International Conference on Computing, Communications and Networking Technologies, ICCCNT 2013

Volumn , Issue , 2013, Pages

  Balachandran, K ^a   Anitha, R ^b  

^a ANNA UNIVERSITY   (India)

^b K S RANGASAMY COLLEGE OF TECHNOLOGY   (India)

Author keywords

Classification; Data mining; Logistic; Lung cancer; Multi layer Perceptron; Pre diagnosis; Random Tree; SMO; Supervised Learning; SVM

Indexed keywords

EID: 84894470369     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICCCNT.2013.6726467     Document Type: Conference Paper

References (16)

1. Fred.R.Hersch, Wilbu.A.Franklin et al., (2000). Early Detection of Lung Cancer: Clinical Perspectives of Recent Advances in Biology and Radiology1. The National Academy of Sciences
2. ICMR.(2006) Cancer Research in ICMR Achievements in Nineties. Periodic Registry report. Bangalore: ICMR
3. K. Kancherla, R. Chilkapatti, S. Mukkamala, J. Cousins, C. Dorian. (2009). Non-Intrusive and Extremely Early Detection of Lung Cancer Using TCPP. Fourth International Multi-Conference on Computing in the Global Information Technology. Page103-108.
4. Cuicc, Yongqian Qiang Youmin Guo Xue Li Qiuping Wanga Hao Chenc Duwu. (2007). The Diagnostic Rules of Peripheral Lung Cancer Preliminary Study Based on Data Mining Technique. Journal of Nanjing Medical University-Elsiever Publication 190-195.
5. Kemal Polat, Salih Günes.(2008). Computer aided medical diagnosis system based on principal component analysis and artificial immune recognition system classifier algorithm. Expert Systems with Applications 34 (2008): 773-779.
6. Jensen, T. Oates and D. (1998). Large datasets lead to overly complex models: An explanation and a solution. Proceedings 4th International Conference on Knowledge discovery and Data mining 294-298.
7. Marko Bohanec, Blaz Zupan, Vladislav Rajkovic. (2000). Applications of qualitative multi-Attribute decision models in health care. International Journal of Medical Informatics 58-59 (2000): 191-205.
8. Maciej A. Mazurowskia, Piotr A. Habasa, Jacek M. Zurada et al., (2008). Training neural network classifiers for medical decision making: The effects of imbalanced datasets on classification performance. Neural Networks 21 2008 Special Issue (2008): 427-436.
9. Yoav Freund Robert E. Schapire, (1999). A Short Introduction to Boosting, Journal of Japanese Society for Artificial Intelligence, 14(5): 771-780.
10. Platt, John C.(1998).Sequential Minimal Optimization: A Fast Algorithm for Training Support Vector Machines. Technical Report MSR-TR-98-14. April 21, 1998.
11. W.Z. Liu, A.P. White, M.T. Hallissey J.W.L. Fielding (2008). Machine learning techniques in early screening for gastric and oesophageal cancer Artificial Intelligence in Medicine 8 (1996) 327-341
12. Breiman, Leo.(1999). Random Forest. Machine Learning 45, 5-32, 2001 (1999): 5-32.
13. Breiman, L. (2000) Some infinity theory for predictor ensembles, Technical Report 579, Statistics Dept. UCB
14. Leo Breiman, (1999). Random Forest. Using adaptive bagging to de-bias regressions, Technical Report 547, Statistics Dept. UCB.
15. Li Cen and Mei,(2008). Application of Hybrid Genetic Algorithm-BP Neural Networks to Diagnosis of Lung Cancer. International Conference on Computer Science and Software Engineering
16. Mingrui Zhang, Yingxu Liu, Yichen Jiang, Zhifu Sun and Ping Yang, (2011). Model Based User Interface Design for Predicting Lung Cancer Treatment Outcomes. 33rd Annual International Conference of the IEEE EMBS Boston, Massachusetts USA, August 30-September 3, 2011