Building a Prediction Model to Predict the Breast Cancer using ANN’S Kernel Based Method

 
 
 
  • Abstract
  • Keywords
  • References
  • PDF
  • Abstract


    The most vulnerable disease is Breast Cancer. Many different  methods and processes were identified for the early detection and for the remedy  of Breast Cancer .Till date only two genes have been identified and which accounts for genetic characteristics to a large extent. A  particular variant has been identified as a major  hetrozygous component which amounts as a major component for the breast cancer. The rigorous working on Human Genome project  has proved that  the family history plays a major role in detecting Breast cancer and also helps in gaining knowledge about  genetic variations which depicts as a high risk factor among all the cancer types. The overall observation has concluded that the risk of this disease is mostly among the women community who has a family history. The ultimate aim is to classify the genes that are most significant and non-significant among all the genes present in the breast cancer tissue at the early stages using    Naïve’s Bayesian’s and c5.0 algorithm and hence build a predictor  model  to   predict breast cancer using ANN’s Kernal based method.

     


  • Keywords


    Mutations, oncology,neural networks,machine learning, kernel based

  • References


      [1] Qing Ping, Christopher C. Yang, Sarah A. Marshall, Nancy E. Avis, and Edward H. Ip “Breast Cancer Symptom Clusters Derived From Social Media and Research Study Data Using Improved K-Medoid Clustering” IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS, VOL. 3, NO. 2, JUNE 2016.

      [2] Ritu Chauhan , Harleen Kaur and

      [3] Mafshar Alam “Data clustering method for Discovering Clusters in

      [4] spatialcancerdatabases”International Journal of Computer Applications (0975-8887) Volume 10-No.6, November 2010.

      [5] Dechang Chen, Kai Xing, Donald Henson, Li Sheng, Arnold M. Schwartz, and Xiuzhen Cheng2 “Developing Prognostic Systems of Cancer Patients by Ensemble Clustering” Hindawi publishing corporation, Journal of Biomedicine and Biotechnology Volume 2009,Article Id 632786.

      [6] S M Halawan ,M Alhaddad and A Ahamad“A study of digital mammograms by using Clustering algorithms” Journal of Scientific & Industrial Research Vol. 71, September 2012, pp. 594-600. Number of Symptoms Risk Store Medium Low No yes No No International Research Journal of Engineering and Technology (IRJET)e-ISSN: 2395-0056 Volume: 02 Issue: 08 | Nov-2015.www.irjet.net p-ISSN: 2395-0072 © 2015, IRJET ISO 9001:2008 Certified Journal Page 1182

      [7] Charles Edeki and Edmand “Comparative Study of Data Mining and Statistical Learning Techniques for PredictionofCancer Survivability” Mediterranean journal of Social Sciences Vol 3November 2012, ISSN: 2039-9340.

      [8] Zakaria Suliman and zubi “Improves Treatment Programs of Breast Cancer using Data Mining Techniques” Journal of Software Engineering and Applications, February 2014, 7, 69-77.

      [9] Labeed K Abdulgafoor and Aji George “Detection of Tumor usingModified K-Means Algorithm and SVM” International Journal ofComputer Applications (0975 – 8887) National Conference on Recent Trends in Computer Applications NCRTCA 2013.

      [10] Sahar and Ala M Elsayad “Predicting the Severity of Breast Masses with Data Mining Methods” International Journal of Computer Science Issues, Vol. 10, Issues 2, No 2, March 2013 ISSN (Print):1694-0814| ISSN (Online):1694-0784 www.IJCSI.org.

      [11] Rajashree Dash , Debahuti Mishra , Amiya Kumar Rath , MiluAcharya “A hybridized K-means clustering approach for high dimensional dataset” International Journal of Engineering, Science and Technology Vol. 2, No. 2, 2010, pp. 59-66.



 

View

Download

Article ID: 19565
 
DOI: 10.14419/ijet.v7i3.34.19565




Copyright © 2012-2015 Science Publishing Corporation Inc. All rights reserved.