IMPETUS TO DIAGNOSIS IN THE FIELD OF ONCOLOGY WITH THE AID OF DATA MINING APPROACH
Keywords:Data mining, Cancer Diagnosis, Cancer Prognosis, Medical Analysis
The extent of data in the area of real life is escalating with the passage of time. So, to excerpt knowledge from such plenty of data is really very much imperative. So to deal with such a huge data and excerpt knowledge is indeed a very convoluted task. In the area of computer science data mining have a number of techniques to deal with such a plenty of data and provide the fruitful excerpt to the user with only a few effortless steps. Such techniques are pertinent to all the field of science. Various research review had been published regarding the applicability of data mining in assorted field of Sciences as like education, banking, insurance, life science, marketing, telecommunications, medicines etc. For the diagnosis of a diseases a number a variety of distinct test had been suggested from the patient. But by the successful data mining approaches such a number of tests can be curtail. Here in this probe we tried to lend and evaluate how various techniques of data mining can be used for prophecy and diagnosis of dominant cancer affliction.
Chaurasia V, Pal S. Data Mining Techniques: To Predict and Resolve Breast Cancer Sur-vivability. International Journal of Computer Science and Mobile Computing 2014; 3: 10-22..
Chang PW and Liou MD. Comparison of three Data Mining techniques with Genetic Algorit-hm in analysis of Breast Cancer data. [Onli-ne]. Available: http://edoc.ypu.edu.tw:8080/paper/ha/Other/%E5%BC%B5%E5%81%89%E6%96%8C_comparison%20of%20data%20mining%20in%20breast%20cancer.pdf
Kharya S. Using Data Mining Techniques for Diagnosis and Prognosis of Cancer Disea-se. International Journal of Computer Scien-ce, Engineering and Information Technology (IJCSEIT) 2012; 2: 55-66..
Senturk ZK, Kara R. Breast Cancer Diagnosis via Data mining: Performance Analysis Of Seven Different Algorithms. Computer Science & Engineering: An International Journal (CSEIJ) 2014; 4: 35-46.
Ghassem Pour S, Mc Leod P, Verma B, Maeder A. Comparing Data Mining with Ensemble Classification of Breast Cancer Masses in Digital Mammograms. 2012; http://ceur-ws.org/Vol-941/aih2012_GhassemPour.pdf.
Rajesh K, Anand S. Analysis of SEER Dataset for Breast Cancer Diagnosis using C4.5 Classification Algorithm. International Journal of Advanced Research in Computer and Co-mmunication Engineering 2012; 1: 72-77
Hota HS. Diagnosis of Breast Cancer Using Intelligent Techniques. International Journal of Emerging Science and Engineering (IJESE) 2013; 1: 45-53.
Gupta S, Kumar D, Sharma A. Data Mining Classification Techniques Applied For Breast Cancer Diagnosis and Prognosis. Indian Jou-rnal of Computer Science and Engineering 2011; 2.
Burke HB, Goodman PH, Rosen DB, Henson DE, Weinstein JN, Harrell FE Jr, Marks JR, Winchester DP & Bostwick DG. Artificial Neural Networks Improve the Accuracy of Cancer Survival Prediction. Cancer 1997; Vol. 79,pp.857-862.
Sumbaly R, Vishnusri N, Jeyalatha S. Diagnosis of Breast Cancer using Decision Tree Data Mining Technique. International Journal of Computer Applications, Volume 2014; 98: 16-24..
Shrivastava SS, Sant A, Aharwal RP. An Overview on Data Mining Approach on Breast Cancer data. International Journal of Advanced Computer Research 2013; 3: 256-262.
Joshi J, Doshi R and Patel J. Diagnosis and Prognosis Breast Cancer Using Classification Rules. International Journal of Engineering Research and General Science 2014; 2: 315-323.
Padmavati J. A Comparative study on Bre-ast Cancer Prediction Using RBF and MLP. International Journal of Scientific & Engineering Research 2011; 2: 1-5.
Aboul HE and Jafar AH. Rough set approach for generation of classification rules of Breast cancer data. Journal Informatica, 2004; 15: 23-38.
Salama GI, Abdelhalim MB, Zeid MA. Breast Cancer Diagnosis on Three Different Datasets Using Multi-Classifiers. International Journal of Computer and Information Technology 2012; 1: 36-43.
Sarvestan SA, Safavi AA, Parandeh MN, Salehi M. Predicting Breast Cancer Survivability using data mining techniques. Journal of Software Technology and Engineering (ICSTE) 2010; 2: 227-231.
Yadav R, Khan Z, Saxena H. Chemotherapy Prediction of Cancer Patient by using Data Mining Techniques. International Journal of Computer Applications 2013; 76: 28-31.
Orlando A, Bruno GC, Susana V, Jorge G, Arlindo OL and Jose R. A Data Mining approach for detection of high-risk Breast Cancer groups. Advances in Soft Computing 2010; 74: 43-51.
Einipour A. A Fuzzy-ACO Method for Detect Breast Cancer. Global Journal of Health Sci-ence 2011; 3: 195-199.
Raad A, Kalakech A, Ayache M. Breast Cancer Classification Using Neural Network Approach: MLP and RBF. The 13th International Arab con¬ference on Information Technology ACIT’12 2012; pp. 15-19.
Kuo WJ, Chang RF, Chen DR, Lee CC. Data min¬ing with Decision Trees for Diagnosis of Breast Tumor in Medical Ultrasonic Images. Breast Cancer Research and Treatment 2001; 66: 51-57.
Majali J, Niranjan R, Phatak V and Tadakhe O. International Journal of Computer Science and Information Technologies (IJCSIT) 2014; 5: 6487-6490.
American Cancer Society. When Cancer Comes Back: Cancer Recurrence. http://www.cancer.org/acs/groups/cid/documents/ webcontent/002947-pdf.pdf.
Kumar R, Verma R. Classification Algorithms for Data Mining: A Survey. International Journal of Innovations in Engineering and Technology (IJIET) 2012; 1: 7-14.
Kesavaraj G, Sukumaran S. A Study on Cla-ssification Techniques in Data Mining. 2012; 1: 4th ICCCNT.
Kumar D, Beniwal S. Genetic Algorithm and Programming Based Classification: A Survey. Journal of Theoretical and Applied Information Technology 2013; 54: 48-58.
Vidhya KA and Aghila G. A Survey of Naïve Bayes Machine Learning approach in Text Document Classification. (IJCSIS) International Journal of Computer Science and Information Security 2012; 7: 206-211.
Bielza C and Larranaga P. Discrete Bayesian Network Classifiers: A Survey. ACM-Transaction 2014; 47: 1.
Sood A. Artificial Neural Networks- Growth & Learn: A Survey. International Journal of Soft Computing and Engineering (IJSCE) 2013; 2: 103-104.
Pradhan A. SUPPORT VECTOR MACHINE-A Survey. International Journal of Emerging Te-chnology and Advanced Engineering 2012; 2: 82-85.
Bhatt J and Patel NS. A Survey on One Class Classification using Ensembles Method. IJIRST-International Journal for Innovative Research in Science and Technology 2014; 1: 19-23.
Zhao Q, Bhowmick S. Association Rule Mining: A Survey. Technical Report, CAIS, Nanyang Technological University, Singapore 2003; No. 2003116.
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