Intuitionistic fuzzy recommender systems: An effective tool for medical diagnosis

Knowledge-Based Systems

Volumn 74, Issue , 2015, Pages 133-150

  Son, Le Hoang ^a   Thong, Nguyen Tho ^a  

^a VIETNAM NATIONAL UNIVERSITY   (Viet Nam)

Author keywords

Accuracy; Fuzzy sets; Intuitionistic fuzzy collaborative filtering; Intuitionistic fuzzy recommender systems; Medical diagnosis

Indexed keywords

COLLABORATIVE FILTERING; DIAGNOSIS; FUZZY LOGIC; MATRIX ALGEBRA; MEDICINE; RECOMMENDER SYSTEMS;

ACCURACY; CLINICAL MEDICINE; COMPOSITION MATRIXES; EFFECTIVE TOOL; INTUITIONISTIC FUZZY; INTUITIONISTIC FUZZY SETS; MEDICAL DATA; MULTI-CRITERIA;

FUZZY SETS;

EID: 84921344806     PISSN: 09507051     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.knosys.2014.11.012     Document Type: Article

References (50)

1. M. Agarwal, M. Hanmandlu, K.K. Biswas, Generalized intuitionistic fuzzy soft set and its application in practical medical diagnosis problem, in: Proceeding of IEEE International Conference on Fuzzy Systems (FUZZ 2011), 2011, pp. 2972-2978.
2. J.Y. Ahn, K.S. Han, S.Y. Oh, C.D. Lee, An application of interval-valued intuitionistic fuzzy sets for medical diagnosis of headache, Int. J. Innovative Comput. Inform. Control 7 (5) (2011) 2755-2762.
3. G. Albeanu, F.L. Popentiu-Vladicescu, Intuitionistic fuzzy methods in software reliability modelling, J. Sustainable Energy 1 (1) (2010) 30-34.
4. K.T. Atanassov, Intuitionistic fuzzy sets, Fuzzy Sets Syst. 20 (1) (1986) 87-96.
5. G. Bernegger, M. Musalek, C. Rehmann-Sutter, An alternative view on the task of prognosis, Critical Rev. Oncol./Hematol. 84 (2012) S17-S24.
6. A.F. Connors et al., The effectiveness of right heart catheterization in the initial care of critically III patients, Jama 276 (11) (1996) 889-897.
7. B.C. Cuong, L.H. Son, H.T.M. Chau, Some context fuzzy clustering methods for classification problems, in: Proceedings of the 2010 ACM Symposium on Information and Communication Technology, 2010, pp. 34-40.
8. D.A. Davis, N.V. Chawla, N. Blumm, N. Christakis, A.L. Barabási, Predicting individual disease risk based on medical history, in: Proceedings of the 17th ACM Conference on Information and Knowledge Management, 2008, pp. 769-778.
9. S.K. De, R. Biswas, A.R. Roy, An application of intuitionistic fuzzy sets in medical diagnosis, Fuzzy Sets Syst. 117 (2) (2001) 209-213.
10. L. Duan, W.N. Street, E. Xu, Healthcare information systems: data mining methods in the creation of a clinical recommender system, Enterprise Inform. Syst. 5 (2) (2011) 169-181.
11. F. Feng, C. Li, B. Davvaz, M.I. Ali, Soft sets combined with fuzzy sets and rough sets: a tentative approach, Soft Comput. 14 (9) (2010) 899-911.
12. F. Feng, X. Liu, V. Leoreanu-Fotea, Y.B. Jun, Soft sets and soft rough sets, Inform. Sci. 181 (6) (2011) 1125-1137.
13. S. Hassan, Z. Syed, From netflix to heart attacks: collaborative filtering in medical datasets, in: Proceedings of the 1st ACM International Health Informatics Symposium, 2010, pp. 128-134.
14. R. Hosseini, T. Ellis, M. Mazinani, J. Dehmeshki, A genetic fuzzy approach for rule extraction for rule-based classification with application to medical diagnosis, in: Proceeding of European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD), 2011, pp. 5-9.
15. W.L. Hung, M.S. Yang, On similarity measures between intuitionistic fuzzy sets, Int. J. Intell. Syst. 23 (3) (2008) 364-383.
16. M. Irfan Ali, A note on soft sets, rough soft sets and fuzzy soft sets, Appl. Soft Comput. 11 (4) (2011) 3329-3332.
17. E. Jafarian, M.A. Rezvani, A valuation-based method for ranking the intuitionistic fuzzy numbers, J. Intell. Fuzzy Syst. 24 (1) (2013) 133-144.
18. R. Kala, R.R. Janghel, R. Tiwari, A. Shukla, Diagnosis of breast cancer by modular evolutionary neural networks, Int. J. Biomed. Eng. Technol. 7 (2) (2011) 194-211.
19. V. Khatibi, G.A. Montazer, Intuitionistic fuzzy set vs. fuzzy set application in medical pattern recognition, Artif. Intell. Med. 47 (1) (2009) 43-52.
20. I. Kononenko, Machine learning for medical diagnosis: history, state of the art and perspective, Artif. Intell. Med. 23 (1) (2001) 89-109.
21. A.R. Meenakshi, M. Kaliraja, An application of interval valued fuzzy matrices in medical diagnosis, Int. J. Math. Anal. 5 (36) (2011) 1791-1802.
22. Meisamshabanpoor, M. Mahdavi, Implementation of a recommender system on medical recognition and treatment, Int. J. e-Education, e-Business, e-Management e-Learning 2 (4) (2012) 315-318.
23. D. Meng, X. Zhang, K. Qin, Soft rough fuzzy sets and soft fuzzy rough sets, Comput. Math. Appl. 62 (12) (2011) 4635-4645.
24. S. Moein, S.A. Monadjemi, P. Moallem, A novel fuzzy-neural based medical diagnosis system, Int. J. Biol. Med. Sci. 4 (3) (2009) 146-150.
25. T.J. Neog, D.K. Sut, An application of fuzzy soft sets in medical diagnosis using fuzzy soft complement, Int. J. Comput. Appl. 33 (2011) 30-33.
26. C.M. Own, Switching between type-2 fuzzy sets and intuitionistic fuzzy sets: an application in medical diagnosis, Appl. Intell. 31 (3) (2009) 283-291.
27. L. Parthiban, R. Subramanian, Intelligent heart disease prediction system using CANFIS and genetic algorithm, Int. J. Biol. Biomed. Med. Sci. 3 (3) (2008) 157-160.
28. Z. Pawlak, Rough sets and intelligent data analysis, Inform. Sci. 147 (1) (2002) 1-12.
29. F. Ricci, L. Rokach, B. Shapira, in: Introduction to Recommender Systems Handbook, Springer, US, 2011, pp. 1-35.
30. R.M. Rodriguez, L. Martinez, F. Herrera, Hesitant fuzzy linguistic term sets for decision making, IEEE Trans. Fuzzy Syst. 20 (1) (2012) 109-119.
31. A.E. Samuel, M. Balamurugan, Fuzzy max-min composition technique in medical diagnosis, Appl. Math. Sci. 6 (35) (2012) 1741-1746.
32. E. Sanchez, Resolution of composition fuzzy relation equations, Inform. Control 30 (1976) 38-48.
33. T.K. Shinoj, S.J. John, Intuitionistic Fuzzy Multi sets and its Application in Medical Diagnosis, World Acad. Sci. Eng. Technol. 6 (2012) 1418-1421.
34. L.H. Son, Enhancing clustering quality of geo-demographic analysis using context fuzzy clustering type-2 and particle swarm optimization, Appl. Soft Comput. 22 (2014) 566-584.
35. L.H. Son, HU-FCF: a hybrid user-based fuzzy collaborative filtering method in recommender systems, Expert Syst. Appl. 41 (15) (2014) 6861-6870.
36. L.H. Son, Optimizing municipal solid waste collection using chaotic particle swarm optimization in GIS based environments: a case study at Danang City, Vietnam, Expert Syst. Appl. 41 (18) (2014) 8062-8074.
37. L.H. Son, DPFCM: a novel distributed picture fuzzy clustering method on picture fuzzy sets, Expert Syst. Appl. 42 (1) (2015) 51-66.
38. L.H. Son, B.C. Cuong, P.L. Lanzi, N.T. Thong, A novel intuitionistic fuzzy clustering method for geo-demographic analysis, Expert Syst. Appl. 39 (10) (2012) 9848-9859.
39. L.H. Son, B.C. Cuong, H.V. Long, Spatial interaction-modification model and applications to geo-demographic analysis, Knowl.-Based Syst. 49 (2013) 152-170.
40. L.H. Son, P.L. Lanzi, B.C. Cuong, H.A. Hung, Data mining in GIS: a novel contextbased fuzzy geographically weighted clustering algorithm, Int. J. Machine Learning Comput. 2 (3) (2012) 235-238.
41. L.H. Son, N.D. Linh, H.V. Long, A lossless DEM compression for fast retrieval method using fuzzy clustering and MANFIS neural network, Eng. Appl. Artif. Intell. 29 (2014) 33-42.
42. E. Szmidt, J. Kacprzyk, Intuitionistic fuzzy sets in some medical applications, in: Proceeding of Computational Intelligence: Theory and Applications, 2001, pp. 148-151.
43. E. Szmidt, J. Kacprzyk, An intuitionistic fuzzy set based approach to intelligent data analysis: an application to medical diagnosis, in: Proceeding of Recent Advances in Intelligent Paradigms and Applications, 2003, pp. 57-70.
44. E. Szmidt, J. Kacprzyk, A similarity measure for intuitionistic fuzzy sets and its application in supporting medical diagnostic reasoning, in: Proceeding of Artificial Intelligence and Soft Computing (ICAISC 2004), 2004, pp. 388-393.
45. K.C. Tan, Q. Yu, C.M. Heng, T.H. Lee, Evolutionary computing for knowledge discovery in medical diagnosis, Artif. Intell. Med. 27 (2) (2003) 129-154.
46. University of California, UCI Repository of Machine Learning Databases, 2007. 〈http://archive.ics.uci.edu/ml/〉.
47. Z. Xiao, X. Yang, Q. Niu, Y. Dong, K. Gong, S. Xia, Y. Pang, A new evaluation method based on D-S generalized fuzzy soft sets and its application in medical diagnosis problem, Appl. Math. Modell. 36 (10) (2012) 4592-4604.
48. Y. Yao, Three-way decisions with probabilistic rough sets, Inform. Sci. 180 (3) (2010) 341-353.
49. L.A. Zadeh, Fuzzy sets, Inform. Control 8 (1965) 338-353.
50. X. Zhang, B. Zhou, P. Li, A general frame for intuitionistic fuzzy rough sets, Inform. Sci. 216 (2012) 34-49.