A knowledge-based system for breast cancer classification using fuzzy logic method

Telematics and Informatics

Volumn 34, Issue 4, 2017, Pages 133-144

  Nilashi, Mehrbakhsh ^a,b   Ibrahim, Othman ^a   Ahmadi, Hossein ^c   Shahmoradi, Leila ^c  

^a UNIVERSITI TEKNOLOGI MALAYSIA   (Malaysia)

^b ISLAMIC AZAD UNIVERSITY   (Iran)

^c TEHRAN UNIVERSITY OF MEDICAL SCIENCES   (Iran)

Author keywords

Breast cancer; CART; Classification; Fuzzy logic; Mammographic mass; Wisconsin Diagnostic Breast Cancer

Indexed keywords

ARTIFICIAL INTELLIGENCE; CLASSIFICATION (OF INFORMATION); CLUSTER ANALYSIS; COMPUTER CIRCUITS; DECISION SUPPORT SYSTEMS; DIAGNOSIS; DISEASES; EXPERT SYSTEMS; FUZZY INFERENCE; FUZZY LOGIC; FUZZY RULES; MAMMOGRAPHY; MAXIMUM PRINCIPLE; PRINCIPAL COMPONENT ANALYSIS;

BREAST CANCER; BREAST CANCER CLASSIFICATIONS; CART; CLASSIFICATION AND REGRESSION TREE; CLASSIFICATION TECHNIQUE; CLINICAL DECISION SUPPORT SYSTEMS; EXPECTATION MAXIMIZATION; MAMMOGRAPHIC MASS;

KNOWLEDGE BASED SYSTEMS;

EID: 85010737695     PISSN: 07365853     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.tele.2017.01.007     Document Type: Article

References (32)

1. Ahmadi Far, A., A new method for breast cancer diagnosis using neural network and genetic algorithms. J. Soft Comput. Decis. Support Syst. 3:5 (2016), 47–49.
2. Bache, K., Lichman, M., 2013. UCI machine learning repository.
3. Bhardwaj, A., Tiwari, A., Breast cancer diagnosis using genetically optimized neural network model. Expert Syst. Appl. 42:10 (2015), 4611–4620.
4. Cattell, R.B., The scree test for the number of factors. Multivariate Behav. Res. 1:2 (1966), 245–276.
5. Chen, C.H., A hybrid intelligent model of analyzing clinical breast cancer data using clustering techniques with feature selection. Appl. Soft Comput. 20 (2014), 4–14.
6. Duijm, L.E.M., Groenewoud, J.H., Jansen, F.H., Fracheboud, J., van Beek, M., de Koning, H.J., Mammography screening in the Netherlands: delay in the diagnosis of breast cancer after breast cancer screening. Br. J. Cancer 91:10 (2004), 1795–1799.
7. Elter, M., Schulz-Wendtland, R., Wittenberg, T., The prediction of breast cancer biopsy outcomes using two CAD approaches that both emphasize an intelligible decision process. Med. Phys. 34:11 (2007), 4164–4172.
8. Han, J., Kamber, M., Data Mining: Concepts and Techniuqes. 2001, Morgan Kaufmann Publishers, San Francisco, CA.
9. Hellendoorn, H., Thomas, C., Defuzzification in fuzzy controllers. J. Intell. Fuzzy Syst. 1:2 (1993), 109–123.
10. Karabatak, M., A new classifier for breast cancer detection based on Naïve Bayesian. Measurement 72 (2015), 32–36.
11. Kharazmi, E., Försti, A., Sundquist, K., Hemminki, K., Survival in familial and non-familial breast cancer by age and stage at diagnosis. Eur. J. Cancer 52 (2016), 10–18.
12. Kohavi, R., 1995. A study of cross-validation and bootstrap for accuracy estimation and model selection. In: Ijcai, vol. 14, no. 2, pp. 1137–1145.
13. Marcano-Cedeño, A., Quintanilla-Domínguez, J., Andina, D., WBCD breast cancer database classification applying artificial metaplasticity neural network. Expert Syst. Appl. 38:8 (2011), 9573–9579.
14. McCarthy, A.M., Yang, J., Armstrong, K., Increasing disparities in breast cancer mortality from 1979 to 2010 for US black women aged 20 to 49 years. Am. J. Public Health, 2015, e1–e3.
15. Mitra, P., Pal, S.K., Siddiqi, M.A., Non-convex clustering using expectation maximization algorithm with rough set initialization. Pattern Recogn. Lett. 24:6 (2003), 863–873.
16. Nauck, D., Kruse, R., Obtaining interpretable fuzzy classification rules from medical data. Artif. Intell. Med. 16:2 (1999), 149–169.
17. Nilashi, M., Jannach, D., bin Ibrahim, O., Ithnin, N., Clustering-and regression-based multi-criteria collaborative filtering with incremental updates. Inf. Sci. 293 (2015), 235–250.
18. Nilashi, M., Ibrahim, O., & Ahani, A., 2016a. Accuracy Improvement for Predicting Parkinson's Disease Progression. Sci. Rep., 6.
19. Nilashi, M., Ibrahim, O.B., Mardani, A., Ahani, A., & Jusoh, A., 2016b. A soft computing approach for diabetes disease classification. Health Inf. J., 1460458216675500.
20. Nilashi, M., Salahshour, M., Ibrahim, O., Mardani, A., Esfahani, M.D., Zakuan, N., A new method for collaborative filtering recommender systems: the case of Yahoo! movies and TripAdvisor datasets. J. Soft Comput. Decis. Support Syst. 3:5 (2016), 44–46.
21. Nilashi, M., Esfahani, M.D., Roudbaraki, M.Z., Ramayah, T., Ibrahim, O., A multi-criteria collaborative filtering recommender system using clustering and regression techniques. J. Soft Comput. Decis. Support Syst. 3:5 (2016), 24–30.
22. Onan, A., A fuzzy-rough nearest neighbor classifier combined with consistency-based subset evaluation and instance selection for automated diagnosis of breast cancer. Expert Syst. Appl., 2015.
23. Pelleg, D., & Moore, A.W., 2000. X-means: extending K-means with efficient estimation of the number of clusters. In: ICML, pp. 727–734.
24. Pendharkar, P.C., Rodger, J.A., Yaverbaum, G.J., Herman, N., Benner, M., Association, statistical, mathematical and neural approaches for mining breast cancer patterns. Expert Syst. Appl. 17:3 (1999), 223–232.
25. Polat, K., Güneş, S., Breast cancer diagnosis using least square support vector machine. Digital Signal Process. 17:4 (2007), 694–701.
26. Quinlan, J.R., Improved use of continuous attributes in C4. 5. J. Artif. Intell. Res. 4 (1996), 77–90.
27. Şahan, S., Polat, K., Kodaz, H., Güneş, S., A new hybrid method based on fuzzy-artificial immune system and k-nn algorithm for breast cancer diagnosis. Comput. Biol. Med. 37:3 (2007), 415–423.
28. Sheikhpour, R., Sarram, M.A., Sheikhpour, R., Particle swarm optimization for bandwidth determination and feature selection of kernel density estimation based classifiers in diagnosis of breast cancer. Appl. Soft Comput., 2015.
29. Übeyli, E.D., Implementing automated diagnostic systems for breast cancer detection. Expert Syst. Appl. 33:4 (2007), 1054–1062.
30. Weng, C.H., Huang, T.C.K., Han, R.P., Disease prediction with different types of neural network classifiers. Telematics Inform. 33:2 (2016), 277–292.
31. Zadeh, L.A., Fuzzy sets. Inf. Control 8:3 (1965), 338–353.
32. Zheng, B., Yoon, S.W., Lam, S.S., Breast cancer diagnosis based on feature extraction using a hybrid of K-means and support vector machine algorithms. Expert Syst. Appl. 41:4 (2014), 1476–1482.