| Peer-Reviewed

Multi-Agent Based Diagnostic Model for Breast Tumour Classification

Received: 5 February 2019     Accepted: 14 March 2019     Published: 10 April 2019
Views:       Downloads:
Abstract

Breast cancer is one of the most hazardous of all types of cancer affecting mainly women. It is the second leading cause of death in Nigerian women. It is difficult to classify breast tumour. The diagnosis of breast cancer on patients in hospitals and clinics is highly subjective and it is reliant on the physician’s expertise. This may often lead to incorrect diagnosis and long waiting time to diagnose breast tumour which may increase the possibility of Cancer metastasizing. This study focused on developing a multi-agent based model for diagnosis of breast tumours using the k-Nearest Neighbor (k-NN) algorithm by classifying the nature of the tumours based on their associated patterns of symptoms and other risk factors of Cancer diseases. A k-NN algorithm using Java and MYSQ was developed to extract and classify the symptoms associated with Breast Cancer. Java Agent Development Environment (JADE) was used for the modeling and simulation. The accuracy score was tested on a breast tumour clinical datasets which were gotten and formed from Federal Medical Centers (FMC) Yola and Gombe in Nigeria. The experimental result of the prediction model shows a percentage accuracy score of 98.9%.

Published in American Journal of Data Mining and Knowledge Discovery (Volume 4, Issue 1)
DOI 10.11648/j.ajdmkd.20190401.11
Page(s) 1-7
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2019. Published by Science Publishing Group

Keywords

Breast Tumour, Multi-Agent, k-NN Algorithm

References
[1] Ponraj, N., Jenifer, E., Poongodi, P. & Manoharan, S. (2012)."Morphological operations for the mammogram image to increase the contrast for the efficient detection of breast cancer", (European Journal of Scientific Research), (ISSN) 1450-216X (68) NO.4 (2012). PP.494-505.
[2] Siegel, R. L., Miller, K. D & Jemal, A. (2017). (A Cancer Journal for Clinicians). Am Cancer Society 67: 7-30. H. Poor, An Introduction to Signal Detection and Estimation. New York: Springer-Verlag, 1985, ch. 4.
[3] Ali, R., Ali, K. & Mohammad, A. (2012). Breast cancer classification using Neural Network approach, the 13th International Arab Conference on Information Technology, ISSN 1812-0857, PP.15-19.C. J. Kaufman, Rocky Mountain Research Lab., Boulder, CO, private communication, May 1995.
[4] Subhas, H. (2013). An Approach to diagnosis of cancer using k-Nearest Neighbour algorithm. A thesis submitted in partial fulfillment of the requirements for the master of computer science and engineering to the department of computer science and engineering, Jadavpur University Kolata.
[5] Hamid, K. & Khani, Z. (2015). A comparative survey on data mining techniques for breast cancer diagnosis and prediction. (Indian Journal of Fundamental and Applied Life Sciences), Vol.5 (S1), pp. 4330-4339. E. H. Miller, “A note on reflector arrays (Periodical style—Accepted for publication),” Engineering Letters, to be published.
[6] Htet, T. & Khin, M. (2015). An approach for Cancer Diagnosis Classification using neural network. Advance Computing: An international Journal (ACIJ), Vol.6, No. 1, PP. 1-11.N. Meghanathan and G. W. Skelton, “Risk Notification Message Dissemination Protocol for Energy Efficient Broadcast in Vehicular Ad hoc Networks,” IAENG International Journal of Computer Science, vol. 37, no. 1, pp. 1–10, Jul. 2010.
[7] Walaa, G. (2016). SVM-K-means: Support Vector Machine based on K-means clustering for Cancer Diagnosis. (International Journal of Computer and Information Technology) (ISSN: 2279 - 0764) Volume 05 – Issue 02, 252-257.
[8] Arpit, B. & Mayur, S. (2017)."Improved k-mean clustering algorithm for prediction analysis using classification technique in data mining", International Journal of Computer Applications. Vol. 157, No 6, pp. 35 – 40.
[9] Autsuo, H. (2018). Diagnosis of breast cancer using decision tree and artificial neural network algorithms. (International Journal of Computer Applications Technology and Research). Volume 7–Issue 01, pp. 23-27.
[10] Dora, L., Agrawal, S., Panda, R., & Abraham, A. (2017), “Optimal breast cancer classification using Gauss–Newton representation based algorithm”, Expert Systems with Applications, 85(1), 134-145.
Cite This Article
  • APA Style

    Yusuf Musa Malgwi, Gregory Maksha Wajiga, Etemi Joshua Garba. (2019). Multi-Agent Based Diagnostic Model for Breast Tumour Classification. American Journal of Data Mining and Knowledge Discovery, 4(1), 1-7. https://doi.org/10.11648/j.ajdmkd.20190401.11

    Copy | Download

    ACS Style

    Yusuf Musa Malgwi; Gregory Maksha Wajiga; Etemi Joshua Garba. Multi-Agent Based Diagnostic Model for Breast Tumour Classification. Am. J. Data Min. Knowl. Discov. 2019, 4(1), 1-7. doi: 10.11648/j.ajdmkd.20190401.11

    Copy | Download

    AMA Style

    Yusuf Musa Malgwi, Gregory Maksha Wajiga, Etemi Joshua Garba. Multi-Agent Based Diagnostic Model for Breast Tumour Classification. Am J Data Min Knowl Discov. 2019;4(1):1-7. doi: 10.11648/j.ajdmkd.20190401.11

    Copy | Download

  • @article{10.11648/j.ajdmkd.20190401.11,
      author = {Yusuf Musa Malgwi and Gregory Maksha Wajiga and Etemi Joshua Garba},
      title = {Multi-Agent Based Diagnostic Model for Breast Tumour Classification},
      journal = {American Journal of Data Mining and Knowledge Discovery},
      volume = {4},
      number = {1},
      pages = {1-7},
      doi = {10.11648/j.ajdmkd.20190401.11},
      url = {https://doi.org/10.11648/j.ajdmkd.20190401.11},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajdmkd.20190401.11},
      abstract = {Breast cancer is one of the most hazardous of all types of cancer affecting mainly women. It is the second leading cause of death in Nigerian women. It is difficult to classify breast tumour. The diagnosis of breast cancer on patients in hospitals and clinics is highly subjective and it is reliant on the physician’s expertise. This may often lead to incorrect diagnosis and long waiting time to diagnose breast tumour which may increase the possibility of Cancer metastasizing. This study focused on developing a multi-agent based model for diagnosis of breast tumours using the k-Nearest Neighbor (k-NN) algorithm by classifying the nature of the tumours based on their associated patterns of symptoms and other risk factors of Cancer diseases. A k-NN algorithm using Java and MYSQ was developed to extract and classify the symptoms associated with Breast Cancer. Java Agent Development Environment (JADE) was used for the modeling and simulation. The accuracy score was tested on a breast tumour clinical datasets which were gotten and formed from Federal Medical Centers (FMC) Yola and Gombe in Nigeria. The experimental result of the prediction model shows a percentage accuracy score of 98.9%.},
     year = {2019}
    }
    

    Copy | Download

  • TY  - JOUR
    T1  - Multi-Agent Based Diagnostic Model for Breast Tumour Classification
    AU  - Yusuf Musa Malgwi
    AU  - Gregory Maksha Wajiga
    AU  - Etemi Joshua Garba
    Y1  - 2019/04/10
    PY  - 2019
    N1  - https://doi.org/10.11648/j.ajdmkd.20190401.11
    DO  - 10.11648/j.ajdmkd.20190401.11
    T2  - American Journal of Data Mining and Knowledge Discovery
    JF  - American Journal of Data Mining and Knowledge Discovery
    JO  - American Journal of Data Mining and Knowledge Discovery
    SP  - 1
    EP  - 7
    PB  - Science Publishing Group
    SN  - 2578-7837
    UR  - https://doi.org/10.11648/j.ajdmkd.20190401.11
    AB  - Breast cancer is one of the most hazardous of all types of cancer affecting mainly women. It is the second leading cause of death in Nigerian women. It is difficult to classify breast tumour. The diagnosis of breast cancer on patients in hospitals and clinics is highly subjective and it is reliant on the physician’s expertise. This may often lead to incorrect diagnosis and long waiting time to diagnose breast tumour which may increase the possibility of Cancer metastasizing. This study focused on developing a multi-agent based model for diagnosis of breast tumours using the k-Nearest Neighbor (k-NN) algorithm by classifying the nature of the tumours based on their associated patterns of symptoms and other risk factors of Cancer diseases. A k-NN algorithm using Java and MYSQ was developed to extract and classify the symptoms associated with Breast Cancer. Java Agent Development Environment (JADE) was used for the modeling and simulation. The accuracy score was tested on a breast tumour clinical datasets which were gotten and formed from Federal Medical Centers (FMC) Yola and Gombe in Nigeria. The experimental result of the prediction model shows a percentage accuracy score of 98.9%.
    VL  - 4
    IS  - 1
    ER  - 

    Copy | Download

Author Information
  • Department of Computer Science, Modibbo Adama University of Technology, Yola, Nigeria

  • Department of Computer Science, Modibbo Adama University of Technology, Yola, Nigeria

  • Department of Computer Science, Modibbo Adama University of Technology, Yola, Nigeria

  • Sections