A novel approach for the diagnosis of diabetes and liver cancer using ANFIS and improved KNN

Research Journal of Applied Sciences, Engineering and Technology

Volumn 8, Issue 2, 2014, Pages 243-250

  Kalaiselvi, C ^a   Nasira, G M ^b  

^a KARPAGAM UNIVERSITY   (India)

^b Chickanna Govt Arts College for Men   (India)

Author keywords

Artificial neural network; K nearest neighbour; Liver cancer; Neuro fuzzy

Indexed keywords

EID: 84908542394     PISSN: 20407459     EISSN: 20407467     Source Type: Journal    
DOI: 10.19026/rjaset.8.966     Document Type: Article

References (25)

1. Andrews, R., J. Diederich and A.B. Tickle, 1995. Survey and critique of techniques for extracting rules from trained artificial neural networks. Knowl-Based Syst., 8(6): 373-389.
2. Asada, N., K. Doi, H. MacMahon, S.M. Montner, M.L. Giger, C. Abe and Y. Wu, 1990. Potential usefulness of an artificial neural network for differential diagnosis of interstitial lung diseases: Pilot study. Radiology, 177: 857-860.
3. Chun-Hong, Z. and X. Wei, 2009. The approach to text automatic classification technology of characteristic databases on the SVM-KNN. Inform. Sci., Vol. 11.
4. Coomans, D. and D.L. Massart, 1982. Alternative k-nearest neighbour rules in supervised pattern recognition. Part 2. Probabilistic classification on the basis of the kNN method modified for direct density estimation. Anal. Chim. Acta, 138: 153-165.
5. Dazzi, D., F. Taddei, A. Gavarini, E. Uggeri, R. Negro and A. Pezzarossa, 2001. The control of blood glucose in the critical diabetic patient: A neuro-fuzzy method. J. Diabetes Complicat., 15(2): 80-87.
6. Dey, R., V. Bajpai, G. Gandhi and B. Dey, 2008. Application of artificial neural network technique for diagnosing diabetes mellitus. Proceeding of 10 Colloquium and the 3rd IEEE Region International Conference on Industrial and Information Systems (ICIIS, 2008), pp: 1-4.
7. Duch, W., R. Adamczak, K. Grabczewski, G. Zal and Y. Hayashi, 2000. Fuzzy and Crisp Logical Rule Extraction Methods in Application to Medical Data. In: Szczepaniak, P.S., P.J.G. Lisboa and J. Kacprzyk (Eds.), Fuzzy System in Medicine. Physica-Verlag, Springer, Berlin, Heidelberg, New York, pp: 593-616.
8. Grenier, P., S. Chevret, C. Beigelman, M.W. Brauner, C. Chastang and D. Valeyre, 1994. Chronic diffuse infiltrative lung disease: Determination of the diagnostic value of clinical data, chest radiography, CT and Bayesian analysis. Radiology, 191: 383-390.
9. Guan, H., T. Kubota, X. Huang, X.S.S. Zhou and M. Turk, 2006. Automatic hot spot detection and segmentation in whole-body FDG-PET images. Proceeding of IEEE International Conference on Image Processing, pp: 85-88.
10. Humar, K. and A. Novruz, 2008. Design of a hybrid system for the diabetes and heart diseases. Expert Syst. Appl., 35: 82-89.
11. Jayalakshmi, T. and A. Santhakumaran, 2010. A novel classification method for classification of diabetes mellitus using artificial neural networks. Proceeding of 2010 International Conference on Data Storage and Data Engineering.
12. Karegowda, A.G., A.S. Manjunath and M.A. Jayaram, 2011. Application of genetic algorithm optimized neural network connection weights for medical diagnosis of PIMA Indians diabetes. Int. J. Soft Comput., 2(2).
13. Karlik, B., M.O. Tokhi and M. Alci, 2003. A fuzzy clustering neural network architecture for multifunction upper-limb prosthesis. IEEE T. Bio-Med. Eng., 50(11): 1255-1261.
14. Kononenko, I., 2001. Machine learning for medical diagnosis: History, state of the art and perspective. Artif. Intell. Med., 23(1): 89-109.
15. Lisboa, J.G., E.C. Ifeachor and P.S. Szczepaniak, Eds. 2000. Artificial Neural Networks in Biomedicine. Springer-Verlag, London, UK.
16. Michie, D., D.J. Spiegelhalter and C.C. Taylor, 1994. Machine Learning, Neural and Statistical Classification. Ellis Horwood, New York.
17. Moreno-Seco, F., L. Mico and J.A. Oncina, 2003. Modification of the LAESA algorithm for approximated k-NN classification. Pattern Recogn. Lett., 24: 47-53.
18. Ngoc, T.N. and S. Edward, year. UCI Machine Learning Repository and Archive. Retrieved form: ics.uci.edu/ml/datasets.html.
19. Patil, B.M., R.C. Joshi and D. Tosniwal, 2010. Hybrid prediction model for type-2 diabetic patients. Expert Syst. Appl., 37: 8102-8108.
20. Richards, G., V.J. Rayward-Smith, P.H. Sönksen, S. Carey and C. Weng, 2000. Data mining for indicators of early mortality in a database of clinical records. Artif. Intell. Med., 22(3): 215-231.
21. Saradhi, G.V., G. Gopalakrishnan, A.S. Roy, R. Mullick, R. Manjeshwar, K. Thielemans and U. Patil, 2009. A framework for automated tumor detection in thoracic FDG PET images using texture-based features. Proceeding of IEEE International Symposium on Biomedical Imaging: From Nano to Macro (ISBI '09), pp: 97-100.
22. Siti, F.B.J. and M.A. Dannawaty, 2005. Diabetes mellitus forecast using artificial neural networks. Proceeding of Asian Conference of Paramedical Research. Kuala Lumpur, Malaysia.
23. Vigneri, P., F. Frasca, L. Sciacca, G. Pandini and R. Vigneri, 2009. Diabetes and cancer. Endocr-Relat. Cancer, 16: 1103-1123.
24. Ying, H., F. Zhou, A. Shields, O. Muzik, D. Wu and E. Heath, 2004. A novel computerized approach to enhancing lung tumor detection in whole body PET images. Proceeding of International Conference on IEEE Eng. Med. Biol. Soc. (EMBS, 2004). San Francisco, CA, pp: 1589-1592.
25. Yuhas, R.H., A.F.H. Goetz and J.W. Boardman, 1992. Discrimination among semi-arid landscape endmembers using the Spectral Angle Mapper (SAM) algorithm. Proceedings of the Summaries of the 4th Annual JPL Airborne Geoscience Workshop, 1: 147-150.