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Usage of Case-Based Reasoning, Neural Network and Adaptive Neuro-Fuzzy Inference System Classification Techniques in Breast Cancer Dataset Classification Diagnosis

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Abstract

Breast cancer is a common to females worldwide. Today, technological advancements in cancer treatment innovations have increased the survival rates. Many theoretical and experimental studies have shown that a multiple classifier system is an effective technique for reducing prediction errors. This study compared the particle swarm optimizer (PSO) based artificial neural network (ANN), the adaptive neuro-fuzzy inference system (ANFIS), and a case-based reasoning (CBR) classifier with a logistic regression model and decision tree model. It also applied three classification techniques to the Mammographic Mass Data Set, and measured its improvements in accuracy and classification errors. The experimental results showed that, the best CBR-based classification accuracy is 83.60%, and the classification accuracies of the PSO-based ANN classifier and ANFIS are 91.10% and 92.80%, respectively.

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References

  1. Mangasarian, O. L., Street, W. N., and Wolberg, W. H., Breast cancer diagnosis and prognosis via linear programming. Oper. Res. 43(4):570–577, 1995.

    Article  MathSciNet  MATH  Google Scholar 

  2. Xiong, X., Kim, Y., Baek, Y., Rhee, D. W., and Kim, S. H., Analysis of breast cancer using data mining & statistical techniques, Sixth International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing, 2005 and First ACIS International Workshop on Self-Assembling Wireless Networks. SNPD/SAWN, pp. 82–87, (2005).

  3. Nilsson, M., and Sollenborn, M., Advancements and trends in medical case-based reasoning: An overview of systems and system development, International Florida Artificial Intelligence Research Society Conference (FLAIRS 2004), Miami Beach, FL(US), 1 pp. 17–19, (2004).

  4. Lieber, J., and Bresson, B., Case-based reasoning for breast cancer treatment decision helping. Proceedings of the 5th European Workshop on Case-Based Reasoning, pp. 173–185, (2000).

  5. Pandey, B., and Mishra, R. B., Knowledge and intelligent computing system in medicine. Comput. Biol. Med. 39(3):215–230, 2009.

    Article  Google Scholar 

  6. Demuth, H., and Beale, M., Neural network toolbox user’s guide for use with MATLAB. MathWorks Inc., Natick, MA, 2006.

    Google Scholar 

  7. Sexton, R. S., Dorsey, R. E., and Sikander, N. A., Simultaneous optimization of neural network function and architecture algorithm. Decis. Support Syst. 36(3):283–296, 2004.

    Article  Google Scholar 

  8. Pandey, B., and Mishra, R. B., Knowledge and intelligent computing system in medicine. Comput. Biol. Med. 39:215–230, 2009.

    Article  Google Scholar 

  9. Jang, J.-S. R., ANFIS: Adaptive-network- based fuzzy inference system. IEEE Trans. Syst. Man Cybern. 23(3):665–685, 1995.

    Article  MathSciNet  Google Scholar 

  10. Riedl, C. C., Pfarl, G., and Helbich, T. H., Breast imaging reporting and data system, http://www.birads.at/.

  11. Elter, M., Schulz-Wendtland, R., and Wittenberg, T., Mammographic Mass Data Set, http://archive.ics.uci.edu/ml/datasets/Mammographic+Mass, 2007.

  12. Mammogram Interpretation: Categories and the ACR/BI-RADS, http://www.imaginis.com/breasthealth/acrbi.asp, (2007).

  13. Schank, R. C., and Abelson, R. P., Scripts, plans, goals, and understanding: An inquiry into human knowledge structures. Erlbaum, Hillsdale, New Jersey, 1977.

    MATH  Google Scholar 

  14. Burke, E. K., MacCarthy, B., Petrovic, S., and Qu, R., Structured cases in case-based reasoning-re-using and adapting cases for time-tabling problems. Knowl.-Based Syst. 13(2–3):159–165, 2000.

    Article  Google Scholar 

  15. Althoff, K. D., Auriol, E., Barletta, R., and Manago, M., A Review of Industrial case-based reasoning tools, An AI perspectives Report. AI Intelligence, United Kingdom, pp. 3–4, 1995.

    Google Scholar 

  16. Zhang, Z., and Yang, Q., Feature weight maintenance in case bases using introspective learning. J. Intell. Inf. Syst. 16(2):95–116, 2001.

    Article  MATH  Google Scholar 

  17. Quinlan, J. R., C4.5: Programs for machine learning. Morgan Kaufmann Publishers, (1993).

  18. Mitra, S., and Hayashi, Y., Neuro-fuzzy rule generation: Survey in soft computing framework. IEEE Trans. Neural Netw. 11(3):748–768, 2000.

    Article  Google Scholar 

  19. Xing, E. P., Jordan, M. I., and Karp, R. M., Feature selection for high-dimensional genomic microarray data, in: Proc. of the 18th International Conference on Machine Learning, pp. 601–608, (2001).

  20. Hosmer, D. W., and Lemeshow, S., Applied logistic regression, 2nd edition. Wiley, New York, 2000.

    Book  MATH  Google Scholar 

  21. Yu, J., Wang, S., and Xi, L., Evolving artificial neural networks using an improved PSO and DPSO. Neurocomputing 71(4–6):1054–1060, 2008.

    Article  Google Scholar 

  22. Kennedy, J., and Eberhart, R. C., Particle swarm optimization. In: IEEE International Conference on Neural Networks. IEEE, New York, pp. 1942–1948, 1995.

    Google Scholar 

  23. Geethanjali, M., Slochanal, S. M. R., and Bhavani, R., PSO trained ANN-based differential protection scheme. Neurocomputing 71(1–3):904–918, 2008.

    Article  Google Scholar 

  24. Parsopoulos, K. E., and Vrahatis, M. N., Recent approaches to global optimization problems through Particle Swarm Optimization. Nat. Comput. 1(2–3):235–306, 2002.

    Article  MathSciNet  MATH  Google Scholar 

  25. Hu, K., and Huang, S. H., Solving inverse problems using Particle Swarm Optimization: An application to aircraft fuel measurement considering sensor failure. Intell. Data Anal. 20(1):421–434, 2007.

    MathSciNet  Google Scholar 

  26. Sadeghi, B. H. M., A BP-neural network predictor model for plastic injection molding process. J. Mater. Process. Technol. 103(3):411–416, 2000.

    Article  MathSciNet  Google Scholar 

  27. Jang, J.-S. R., Sun, C. T., and Mizutani, E., Neuro-Fuzzy and soft computing: A computational approach to learning and machine intelligence, Prentice Hall; US Ed edition Prentice-Hall, (1996).

  28. Papadimitriou, S., and Terzidis, K., Efficient and interpretable fuzzy classifiers from data with support vector learning. Intell. Data Anal. 9(6):527–550, 2005.

    Google Scholar 

  29. Mohammad, R., Akbarzadeh, T., and Majid, M. K., A hierarchical fuzzy rule-based approach to aphasia diagnosis. J. Biomed. Inform. 40(5):465–475, 2007.

    Article  Google Scholar 

  30. Castellano, G., Fanelli, A. M., and Mencar, C., An empirical risk functional to improve learning in a neuro-fuzzy classifier. IEEE Trans. Syst. Man Cybern. Part B-Cybern. 34(1):725–731, 2004.

    Article  Google Scholar 

  31. Ghazavi, S. N., and Liao, T. W., Medical data mining by fuzzy modeling with selected features. Artif. Intell. Med. 43(3):195–206, 2008.

    Article  Google Scholar 

  32. Swets, J. A., Measuring the accuracy of diagnostic systems. Science 240(4857):1285–1293, 1988.

    Article  MathSciNet  MATH  Google Scholar 

  33. Bradley, A. P., The use of the area under the ROC curve in the evaluation of machine learning algorithms. Pattern Recognit. 30(7):1145–1159, 1997.

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Industrial Engineering and Management, National Chin-Yi University of Technology, 35, Lane 215, Section 1, Chungshan Road, Taiping, Taichung, 411, Taiwan, Republic of China

    Mei-Ling Huang, Yung-Hsiang Hung & Tzu-Hao Wang

  2. Department of Industrial Engineering & Management, National Chiao Tung University, 1001 Ta Hsueh Road, Hsinchu, Taiwan, 300, Republic of China

    Wen-Ming Lee & R. K. Li

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  1. Mei-Ling Huang
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  2. Yung-Hsiang Hung
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Correspondence to Mei-Ling Huang.

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Huang, ML., Hung, YH., Lee, WM. et al. Usage of Case-Based Reasoning, Neural Network and Adaptive Neuro-Fuzzy Inference System Classification Techniques in Breast Cancer Dataset Classification Diagnosis. J Med Syst 36, 407–414 (2012). https://doi.org/10.1007/s10916-010-9485-0

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  • DOI: https://doi.org/10.1007/s10916-010-9485-0

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