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Home»Articles»A Study: Breast Cancer Prediction Using Data Mining Techniques

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Asian Journal of Computer Science and Technology (AJCST)

Editor Dr. K. Ganesh
Print ISSN : 2249-0701
Frequency : Quarterly

A Study: Breast Cancer Prediction Using Data Mining Techniques

Author : B. Gousbi and A. R. Mohamed Shanavas
Volume 8 No.2 Special Issue:March 2019 pp 52-56

Abstract

Data mining is the extraction of unseen predictive info from huge databases, is the process of arranging through enormous data sets to recognize patterns and create relationships to resolve the problems through data analysis. Cancer is one of the primary reasons of death wide-reaching. Timely detection and prevention of cancer plays a very vital role in decreasing deaths affected by cancer. Identification of genetic and environmental factors is very significant in emerging novel methods to identify and avert cancer. Many researchers’ use data mining techniques like clustering, classification and prediction find potential cancer patients. This paper focuses on a breast cancer prediction system built on data mining techniques. With the help of this system, people can guess the possibility of the breast cancer in the former stage itself.

Keywords

Data Mining, Breast Cancer, Prediction, Classification and Clustering

Full Text:

References

[1] Hemlata Sahu, Shalini Shrma, and Seema Gondhalakar, “A Brief Overview on Data Mining Survey”, International Journal of Computer Technology and Electronics Engineering (IJCTEE), Vol. 1, No.3, pp. 114-121, 2011. [2] P. Ramachandran, N. Girija, and T. Bhuvaneswari, “Early Detection and Prevention of Cancer using Data Mining Techniques”,International Journal of Computer Applications, Vol. 97, No. 13, pg. 48-53, July 2014.
[3] K. Arutchelvan and R. Periyasamy, “Cancer Prediction System Using Data Mining Techniques”, International Research Journal of Engineering and Technology, Vol.2, No. 8, pp. 1179-1183, Nov. 2015.
[4] V. Krishnaiah, G. Narsimha, and N. Subhash Chandra, “Diagnosis of Lung Cancer Prediction System Using Data Mining Classification Techniques”, International Journal of Computer Science and Information Technologies, Vol.4, No.1, pp. 39-45, 2013.
[5] Sahar Mokhtar and Alaa. M. Elsayad, “Predicting the Severity of Breast Masses with Data Mining Methods”, International Journal of Computer ScienceIssues, Vol. 10, Mar. 2013.
[6] Ada and Rajneet Kaur, “Using Some Data Mining Techniques to Predict the Survival Year of Lung Cancer Patient”, International Journal of Computer Science and Mobile Computing, Vol. 2, No. 4, pp.1-6, Apr. 2013.
[7] Charles Edeki and Shardul Pandya, “Comparative Study of Data Mining and Statistical Learning Techniques for Prediction of Cancer Survivability”, Mediterranean journal of Social Sciences., Vol. 3, No. 14, pp. 49-56, Nov. 2012.
[8] Zakaria Sulimanzubi and Rema Asheibani Saad, “Improves Treatment Programs of Lung Cancer using Data Mining Techniques”,
Journal of Software Engineering and Applications, Vol. 7, pp. 69-77, Feb. 2014. [9] Shivangi Bhardwaj, “Data Mining Clustering Techniques – A Review”, International Journal of Computer Science and Mobile Computing, Vol. 6, No.5, pp. 183-186, May. 2017.
[10] C. Kalaiselvi and G.M. Nasira, “A New Approach for Diagnosis of Diabetes and Prediction of Cancer using ANFIS”, World Congress on Computing and Communication Technologies, pp. 188-190, 2014.
[11] G. Ravi Kumar, G. A. Ramachandra and K. Nagamani, “An Efficient Prediction of Breast Cancer Data using Data Mining Techniques”, International Journal of Innovations in Engineering and Technology, Vol. 2, No. 4, pp. 139-144, Aug. 2013.
[12] Vikas Chaurasia and Saurabh Pal, “Data Mining Techniques: To Predict and Resolve Breast Cancer Survivability”, International Journal of Computer Science and Mobile Computing, Vol. 3, No.1, pp. 10-22, Jan. 2014.
[13] K. Sivakami, “Mining Big Data: Breast Cancer Prediction using DT – SVM Hybrid Model”, International Journal of Scientific Engineering and Applied Science, Vol. 1, No. 5, pp. 418-429, Aug. 2015.

Asian Journal of Computer Science and Technology is a peer-reviewed international journal that publishes high-quality scientific articles (both theory and practice) and research papers covering all aspects of future computer and Information Technology areas. Topics include, but are not limited to:

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Data mining is the extraction of unseen predictive info from huge databases, is the process of arranging through enormous data sets to recognize patterns and create relationships to resolve the problems through data analysis. Cancer is one of the primary reasons of death wide-reaching. Timely detection and prevention of cancer plays a very vital role in decreasing deaths affected by cancer. Identification of genetic and environmental factors is very significant in emerging novel methods to identify and avert cancer. Many researchers’ use data mining techniques like clustering, classification and prediction find potential cancer patients. This paper focuses on a breast cancer prediction system built on data mining techniques. With the help of this system, people can guess the possibility of the breast cancer in the former stage itself.

Editor-in-Chief
Dr. K. Ganesh
Global Lead, Supply Chain Management, Center of Competence and Senior Knowledge
Expert at McKinsey and Company, India
[email protected]
Editorial Advisory Board
Dr. Eng. Hamid Ali Abed AL-Asadi
Department of Computer Science, Basra University, Iraq
[email protected]
Dr. Norjihan Binti Abdul Ghani
Department of Information System, University of Malaya, Malaysia
[email protected].edu.my
Dr. Christos Bouras
Department of Computer Engineering & Informatics, University of Patras, Greece
[email protected]
Dr. Maizatul Akmar Binti Ismail
Department of Information System, University of Malaya, Malaysia
[email protected]
Dr. Harold Castro
Department of Systems Engineering and Computing, University of the Andes, Colombia
[email protected]
Dr. Busyairah Binti Syd Ali
Department of Software Engineering, University of Malaya, Malaysia
[email protected]
Dr. Sri Devi Ravana
Department of Information system, University of Malaya, Malaysia
[email protected]
Dr. Karpaga Selvi Subramanian
Department of Computer Engineering, Mekelle University, Ethiopia
[email protected]
Dr. Mazliza Binti Othman
Department of Computer System & Technology, University of Malaya, Malaysia
[email protected]
Dr. Chiam Yin Kia
Department of Software Engineering, University of Malaya, Malaysia
[email protected]
Dr. OUH Eng Lieh
Department of Information Systems, Singapore Management University, Singapore
[email protected]

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    Editorial Note

    Editorial Dr. K. Ganesh

    Editor-in-Chief
    Dr. K. Ganesh
    Global Lead, Supply Chain Management, Center of Competence and Senior Knowledge
    Expert at McKinsey and Company, India
    [email protected]
    Editorial Advisory Board
    Dr. Eng. Hamid Ali Abed AL-Asadi
    Department of Computer Science, Basra University, Iraq
    [email protected]
    Dr. Norjihan Binti Abdul Ghani
    Department of Information System, University of Malaya, Malaysia
    [email protected]
    Dr. Christos Bouras
    Department of Computer Engineering & Informatics, University of Patras, Greece
    [email protected]
    Dr. Maizatul Akmar Binti Ismail
    Department of Information System, University of Malaya, Malaysia
    [email protected]
    Dr. Harold Castro
    Department of Systems Engineering and Computing, University of the Andes, Colombia
    [email protected]
    Dr. Busyairah Binti Syd Ali
    Department of Software Engineering, University of Malaya, Malaysia
    [email protected].edu.my
    Dr. Sri Devi Ravana
    Department of Information system, University of Malaya, Malaysia
    [email protected]
    Dr. Karpaga Selvi Subramanian
    Department of Computer Engineering, Mekelle University, Ethiopia
    [email protected]
    Dr. Mazliza Binti Othman
    Department of Computer System & Technology, University of Malaya, Malaysia
    [email protected]
    Dr. Chiam Yin Kia
    Department of Software Engineering, University of Malaya, Malaysia
    [email protected]
    Dr. OUH Eng Lieh
    Department of Information Systems, Singapore Management University, Singapore
    [email protected]

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