CFP last date
22 April 2024
Reseach Article

Supervised Learning Processing Techniques for Pre-Diagnosis of Lung Cancer Disease

by K.Balachandran, R.Anitha
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 1 - Number 5
Year of Publication: 2010
Authors: K.Balachandran, R.Anitha
10.5120/130-247

K.Balachandran, R.Anitha . Supervised Learning Processing Techniques for Pre-Diagnosis of Lung Cancer Disease. International Journal of Computer Applications. 1, 5 ( February 2010), 17-21. DOI=10.5120/130-247

@article{ 10.5120/130-247,
author = { K.Balachandran, R.Anitha },
title = { Supervised Learning Processing Techniques for Pre-Diagnosis of Lung Cancer Disease },
journal = { International Journal of Computer Applications },
issue_date = { February 2010 },
volume = { 1 },
number = { 5 },
month = { February },
year = { 2010 },
issn = { 0975-8887 },
pages = { 17-21 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume1/number5/130-247/ },
doi = { 10.5120/130-247 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T19:44:20.366345+05:30
%A K.Balachandran
%A R.Anitha
%T Supervised Learning Processing Techniques for Pre-Diagnosis of Lung Cancer Disease
%J International Journal of Computer Applications
%@ 0975-8887
%V 1
%N 5
%P 17-21
%D 2010
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Lung cancer disease is one of the dreaded disease is leading cause of death among men in developed and developing countries. Its cure rate and prognosis depends mainly on the early detection and diagnosis of the disease. Creating awareness among the general public about the disease and screening probable impact group requires lot of painstaking effort. This paper mainly focuses on selectively screening susceptible people for pre-diagnosis of Lung cancer disease. The approach adopted here is, conceptualizing artificial neural network model, based on statistical parameters based on cancer registry, symptoms and Risk factors. Supervisory delta learning approach is used to train the model. The model is developed using multi layer perceptron network and trained by established Lung cancer data. This model is then used for the test data. Tested data is again compared with the clinical diagnosed report and the model is reconfigured by including the current information and new training weights are computed.

References
  1. Sang Min Park, Min Kyung Lim, Soon Ae Shin & Young Ho Yun 2006. Impact of prediagnosis smoking, Alcohol, Obesity and Insulin resistance on survival in Male cancer Patients: National Health Insurance corporation study. Journal of clinical Oncology, Vol 24 Number 31 November 2006
  2. Yongqian Qiang, Youmin Guo, Xue Li, Qiuping Wang, Hao Chen, & Duwu Cuic 2007 .The Diagnostic Rules of Peripheral Lung cancer Preliminary study based on Data Mining Technique. Journal of Nanjing Medical University, 21(3): 190-195
  3. Murat Karabhatak, M.Cevdet Ince 2008. Expert system for detection of breast cancer based on association rules and neural network. Journal: Expert systems with Applications
  4. ICMR Report 2006. Cancer Research in ICMR Achievements in Nineties
  5. Ta-Cheng Chen, Tung-Chow Hsu 2006. A GAs based approach for mining breast cancer pattern. Journal: Expert systems with Applications 30(2006) 674-681
  6. Petra Perner 1992. Mining Knowledge in X-ray images for Lung cancer diagnosis. Journal: Computer vision and applied computer Sciences
  7. W.Z.Liu, A.P.White, M.T.Hallissey, J.W.LFielding 1995. Machine learning techniques in early screening for gastric and esophageal cancer. Artificial Intelligence in Medicine 8(1996) 327-341
  8. Edward H.Shortliffe, A.Carlisle Scott, Miriam B.Bischoff, A.Bruce Campbell, William vanMelle, Charlotte D Jacobs ~1982 . Oncocin: An Expert system for Oncology protocol Management. Proceedings of the 7th IJCAI 1981
  9. Edward H.Shortliffe, A.Carlisle Scott, Miriam B.Bischoff, A.Bruce Campbell, William vanMelle, Charlotte D Jacobs 1981. An Expert system for Oncology protocol Management. Proceedings of the 7th IJCAI 1981 chapter 35 653-665
  10. James S.Gordon 2008 . Mind-body, Medicine and Cancer. Journal: Hematology Oncology clinic N.America 22(2008) 683-708
  11. Kemal Polat, Salih Gunes 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
  12. Ira J. Kalet, Mrk Whipple. Silvia Pessah, Jerry Barker. Mary M. Austin Seymour, Linda G.Shapiro 2002. A Rule based model for Local and Regional Tumor Spread. Journal: Artificial Intelligence in Medicine
  13. Astrid Pozet, Virginie Westeel, Pascal Berion, Arlettte Danzon, Didier Dabieuvre, Jean-Luc Beton, Alain Monnier, Jean Lahourcade, Jean-Charles Daphin, Mriette Mercier 2008. Rurality and survival differences in Lung cancer: A large population-based Multivariate analysis. Journal: Lung cancer (2008) 59, 291-300
  14. Ahmed Besaratinia, Gerd P Pfeifer 2008. Second-hand smoke and human lung cancer. Review article http://oncology.thelancet.com Vol 9 Jul-2008
  15. Maria Jose de Paula Castnha, Laecio Carvalho de Barros, Akebo Yamakami, Laercio Luis Vendite 2008. Fuzzy Expert system: An example in prostate cancer. Applied Mathematics and Computation 202(2008) 78-85
  16. 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
  17. Maciej A.Mazrowski, Pior A Habas, Jacek Zurada, George D Tourassi 2008. Decision optimization of case based computer aided decision systems using genetic algorithms with application to mammography. Physics in Medicine and Biology 53(2008) 895-908
  18. Curtis P.Langlotz, Lawrence M. Fagan, Samson W.Tu, Branimir I.Sikic Edward H.Shortliffe 1987. A Therapy Planning Architecture that combines Decision Theory and Artificial Intelligence Techniques. Computers and Biomedical Research 20, 279-303 (1987)
  19. Maciej A.Mazrowski, Pior A Habas, Jacek Zurada, Joseph Y.Lo, Jay A.Baker, George D Tourassi 2008. Training neural network classifiers for medical decision making: The effects of imbalanced datasets on classifier performance. Neural networks 21(2008)427-436, Special Issue
  20. WLodzizlaw Duch, Rudy Setiona, Jacek M.Zurada 2004. Computational Intelligence Methods for Rule-based data Understanding. Proceedings of the IEEE Vol-92 No. 5 , 771-805 May-2004
  21. Yanfeng Hou, Jacek M.Zurada, Waldemar Karwowski, William S.Marras, Kermit Davis 2007. Identification of key variables using Fuzzy average with Fuzzy cluster distribution. IEEE transactions on fuzzy systems vol.15 No. 4 Aug-2007 673-685
  22. Maciej A.Mazrowski, Jacek Zurada, George D Tourassi 2008. Selection of samples in case based computer aided decision systems. Physics in Medicine and Biology 53(2008) 6079-6088
  23. Alex L. Tay , Jacek M.Zurada, Lai Ping Wong and Jian Xu 2007. The Hierarchical fast learning artificial Neural Network (HieFLANN) - An autonomous platform for Hierarchical Neural Network construction. IEEE Transactions on Neural Networks Vol. 18 No. 6 Nov-2007 1645-1657
  24. Maciej Majewski, Jacek M.Zurada 2008. Sentence recognition using artificial neural networks. Knowledge based systems 21(2008) 629-635
  25. C-Q Zhu, W Shih, C-H Ling, M-S Tsao 2006. Immunohistochemical markers of prognosis in non-small cell lung cancer: a review and proposal for a multiphase approach to marker evaluation. Journal of Clinical Pathology 2006;59:790-800
  26. Consolidated report of population based cancer registries 2001-04, Incidence and Distribution of cancer, ICMR report, Bangalore
Index Terms

Computer Science
Information Sciences

Keywords

Lung cancer Non-small cell Small cell Perceptron neural network Supervisory learning Delta Learning Reinforcement learning