CFP last date
20 January 2025
Reseach Article

An Improved Pattern Mining Technique for Analysis of Prognostication of Breast Carcinoma Disease

by Harshnika Bhasin, Zuber Farooqui
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 123 - Number 7
Year of Publication: 2015
Authors: Harshnika Bhasin, Zuber Farooqui
10.5120/ijca2015905440

Harshnika Bhasin, Zuber Farooqui . An Improved Pattern Mining Technique for Analysis of Prognostication of Breast Carcinoma Disease. International Journal of Computer Applications. 123, 7 ( August 2015), 41-45. DOI=10.5120/ijca2015905440

@article{ 10.5120/ijca2015905440,
author = { Harshnika Bhasin, Zuber Farooqui },
title = { An Improved Pattern Mining Technique for Analysis of Prognostication of Breast Carcinoma Disease },
journal = { International Journal of Computer Applications },
issue_date = { August 2015 },
volume = { 123 },
number = { 7 },
month = { August },
year = { 2015 },
issn = { 0975-8887 },
pages = { 41-45 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume123/number7/21975-2015905440/ },
doi = { 10.5120/ijca2015905440 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:12:04.941632+05:30
%A Harshnika Bhasin
%A Zuber Farooqui
%T An Improved Pattern Mining Technique for Analysis of Prognostication of Breast Carcinoma Disease
%J International Journal of Computer Applications
%@ 0975-8887
%V 123
%N 7
%P 41-45
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

This paper presents a study of different techniques of information mining algorithms used for the aim of predicting carcinoma because it is understood to any or all that prediction of carcinoma survivability has been a difficult research problem for several researchers. Since the early dates of the related analysis, a lot of advancement has been recorded in many related fields. For an instance, a sincere thanks to existing biomedical technologies, higher instructive prognostic factors are being measured and recorded; because of low value computer components and software system technologies, high volume good quality information is being collected and keep automatically; and at last thanks to higher analytical strategies, those voluminous information is being processed effectively and with efficiency. Therefore, the most objective of this manuscript is to report on a research project where we have a tendency to take advantage of these available technological advancements to develop prediction models for carcinoma survivability.

References
  1. Rafael C. Gonzalez, Richard E. Woods. “Digital Image Processing”, New Jersey. Prentice Hall, 2002.
  2. Delen D, Patil N. Knowledge extraction from prostate cancer data. The 39thAnnual Hawaii International Conference on System Sciences; 2006; 1-10.
  3. National Cancer Institute. Surveillance, Epidemiology, and End Results (SEER) Program Public-Use Data (1973-2008). Cancer Statistics Branch; 2011.
  4. Nevine M. Labib, and Michael N. Malek, “Data Mining for Cancer Management in Egypt Case Study: Childhood Acute Lymphoblastic Leukemia”, World Academy of Science, Engineering and Technology 8 2005
  5. G. A Forgionne, A. Gagopadhyay, and M. Adya, “Cancer Surveillance Using Data Warehousing, Data Mining, and Decision Support Systems”, Topics in Health Information Management, vol. 21(1); Proquest Medical Library, August 2000
  6. W. Kuo, R. Chang, D. Chen and C. C. Lee, “Data Mining with Decision Trees for Diagnosis of Breast Tumor in Medical Ultrasonic Images”, Breast Cancer Research and Treatment, Dordrecht, vol. 66, Iss. 1, Mar 2001.
  7. Pascal Boilot, Evor L. Hines, Julian W. Gardner, Member, IEEE, Richard Pitt, Spencer John, Joanne Mitchell, and David W. Morgan, “Classification of Bacteria Responsible for ENT and Eye Infections Using the Cyranose System”, IEEE SENSORS JOURNAL, vol. 2, NO. 3, JUNE 2002.
  8. A.Sudha “Utilization of Data mining Approaches for Prediction of Life Threatening Diseases Survivability” International Journal of Computer Applications (0975 – 8887) Volume 41– No.17, March 2012.
  9. K.Balachandran, Dr.R.Anitha “Supervised Learning Processing Techniques for Pre-Diagnosis of Lung Cancer Disease” ©2010 International Journal of Computer Applications (0975 – 8887) Volume 1 – No. 4.
  10. Delen D, Walker G, Kadam A. Predicting breast cancer survivability: a comparison of three data mining methods. Artificial Intelligence in Medicine. 2005 Jun; 34(2):113-27.
  11. Tan AC, Gilbert D. “Ensemble machine learning on gene expression data for cancer classification”, Appl Bioinformatics. 2003;2(3 Suppl):S75-83.
  12. Liu Ya-Qin, Wang Cheng, Zhang Lu,” Decision Tree Based Predictive Models for Breast Cancer Survivability on Imbalanced Data” , 3rd International Conference on Bioinformatics and Biomedical Engineering , 2009.
  13. JinyanLiHuiqing Liu, See-Kiong Ng and Limsoon Wong,” Discovery of significant rules for classifying cancer diagnosis data”, Bioinformatics 19(Suppl. 2)Oxford University Press 2003.
  14. Dong-Sheng Cao, Qing-Song Xu ,Yi-Zeng Liang, Xian Chen, “Automatic feature subset selection for decision tree-based ensemble methods in the prediction of bioactivity”, Chemometrics and Intelligent Laboratory Systems.
  15. My ChauTu, Dongil Shin, Dongkyoo Shin ,“Effective Diagnosis of Heart Disease through Bagging Approach”, 2nd International Conference on Biomedical Engineering and Informatics,2009.
  16. My ChauTu, Dongil Shin, Dongkyoo Shin, “A Comparative Study of Medical Data Classification Methods Based on Decision Tree and Bagging Algorithms” Eighth IEEE International Conference on Dependable, Autonomic and Secure Computing, 2009.
  17. Tsirogiannis, G.L, Frossyniotis, D, Stoitsis, J, Golemati, S, Stafylopatis, A Nikita,K.S,” Classification of Medical Data with a Robust Multi-Level Combination scheme”, IEEE international joint Conference on Neural Networks.
  18. Pan Wen, “Application of decision tree to identify a abnormal high frequency electrocardiograph”, China National Knowledge Infrastructure Journal, 2000.
  19. Kaewchinporn .C, Vongsuchoto. N, Srisawat. A ” A Combination of Decision Tree Learning and Clustering for Data Classification”, 2011 Eighth International Joint Conference on Computer Science and Software Engineering (JCSSE).
  20. V. Chauraisa and S. Pal, “Data Mining Approach to Detect Heart Diseases”, International Journal of Advanced Computer Science and Information Technology (IJACSIT),Vol. 2, No. 4,2013, pp 56-66.
  21. V. Chauraisa and S. Pal, “Early Prediction of Heart Diseases Using Data Mining Techniques”, Carib.j.SciTech,,Vol.1, pp. 208-217, 2013.
Index Terms

Computer Science
Information Sciences

Keywords

Cancer prediction Breast cancer detection hybridapriori association rule mining pattern analysis.