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A Novel Clustering Algorithm in a Neutrosophic Recommender System for Medical Diagnosis

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Abstract

Decision-making processes have been extensively used in artificial intelligence and cognitive sciences to explain and improve individual and social perception. As one of the most typical decision-making problems, medical diagnosis is used to analyze the relationship between symptoms and diseases according to uncertain and inconsistent information. It is essential to investigate the structure of a set of records on different levels such that similar patients can be treated concurrently within a group. In this paper, we propose a novel clustering algorithm in a neutrosophic recommender system for medical diagnosis. First, we define new algebraic structures for the system such as lattices, De Morgan algebra, Kleen algebra, MV algebra, BCK algebra, Stone algebra, and Brouwerian algebra. Based on these algebraic structures, we construct a neutrosophic recommender similarity matrix and a neutrosophic recommender equivalence matrix. A consecutive series of compositions between the neutrosophic recommender similarity matrices is performed to obtain the neutrosophic recommender equivalence matrix. From this matrix, a λ-cutting matrix is defined to conduct clustering among the neutrosophic recommender systems. Regarding the values of clustering validity indices, the Davies-Bouldin (DB) of the proposed method is approximately 20% better than those of the methods of Sahin (Neutrosophic Sets and Systems. 2014;2:18–24), Ye (J Intell Syst. 2014;23(4):379–89), and Ye (Soft Computing 2016;1–7). Analogously, the IFV and simplified silhouette with criterion (SSWC) of the proposal are better than those of the relevant methods with the improvement percentages being 30 and 70%, respectively. The results show that the proposed method is better than the related algorithms in terms of clustering quality whilst its computational time is slightly slower. The contributions of this research is significant in both algorithmic aspects of computational intelligence and and practical applications.

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Abbreviations

NS:

Neutrosophic set

SNS:

Simplified neutrosophic set

SVNS:

Single-valued neutrosophic set

RS:

Recommender system

NRS:

Neutrosophic recommender system

SC-NRS:

Single-criterion neutrosophic recommender system

MC-NRS:

Multi-criteria neutrosophic recommender system

NRM:

Neutrosophic recommender matrix

NEM:

Neutrosophic equivalence matrix

NRSD:

Neutrosophic recommender similarity degree

NRSM:

Neutrosophic recommender similarity matrix

NREM:

Neutrosophic recommender equivalence matrix

RHC:

Right heart catheterization

DMD:

Duchenne muscular dystrophy

DB:

Davies-Bouldin

SSWC:

Simplied silhouete width criterion

IFV:

A spatial validity index

References

  1. Akhtar N, Agarwal N, Burjwal A. K-mean algorithm for image segmentation using neutrosophy. In: IEEE international conference on advances in computing, communications and informatics (ICACCI 2014); 2014. p. 2417–21.

  2. Ali M, Son L.H., Thanh N.D, Minh N.V. A neutrosophic recommender system for medical diagnosis based on algebraic neutrosophic measures. Appl Soft Comput (under 3rd revision). 2017.

  3. Broumi S, Smarandache S. Extended Hausdorff distance and similarity measure of refined neutrosophic sets and their application in medical diagnosis. Journal of New Theory. 2015;1(7):64–78.

    Google Scholar 

  4. Connors FA et al. The effectiveness of right heart catheterization in the initial care of critically III patients. Jama. 1996;276(11):889–97.

  5. Davis DA, Chawla NV, Blumm N, Christakis N, Barabási AL. Predicting individual disease risk based on medical history. In: Proceedings of the 17th ACM conference on information and knowledge management; 2008. p. 769–78.

  6. Department of Biostatistics. Vanderbilt University. Available at: http://biostat.mc.vanderbilt.edu/DataSets. 2016.

  7. Ding S, Zhang J, Jia H, Qian J. An adaptive density data stream clustering algorithm. Cogn Comput. 2016;8(1):30–38.

    Article  Google Scholar 

  8. Duan L, Street WN, Xu E. Healthcare information systems: data mining methods in the creation of a clinical recommender system. Enterprise Inform Syst. 2011;5(2):169–81.

    Article  Google Scholar 

  9. Farhadinia B, Xu Z. Distance and aggregation-based methodologies for hesitant fuzzy decision making. Cogn Comput. 2017. doi:10.1007/s12559-016-9436-2.

  10. George JK, Bo Y. Fuzzy sets and fuzzy logic: theory and applications. Upper Saddle River, New Jersey: Prentice Hall; 1995.

    Google Scholar 

  11. Guo Y, Zhou C, Chan HP, Chughtai A, Wei J, Hadjiiski LM, Kazerooni EA. Automated iterative neutrosophic lung segmentation for image analysis in thoracic computed tomography. Med Phys 2013;40(8). doi:10.1118/1.4812679.

  12. Guo Y, Sengur A. NCM: neutrosophic c-means clustering algorithm. Pattern Recogn. 2015;48(8):2710–24.

    Article  Google Scholar 

  13. Hassan S, Syed Z. From netflix to heart attacks: collaborative filtering in medical datasets. In: Proceedings of the 1st ACM international health informatics symposium; 2010. p. 128–34.

  14. Hong C, Yu J, Wan J, Tao D, Wang M. Multimodal deep autoencoder for human pose recovery. IEEE Trans Image Process. 2015;24(12):5659–70.

  15. Hong C, Yu J, Tao D, Wang M. Image-based three-dimensional human pose recovery by multiview locality-sensitive sparse retrieval. IEEE Trans Ind Electron. 2015;62(6):3742–51.

    Google Scholar 

  16. Jia H, Ding S, Du M. Self-tuning p-spectral clustering based on shared nearest neighbors. Cogn Comput. 2015;7(5):622–32.

    Article  Google Scholar 

  17. Kala R, Janghel RR, Tiwari R, Shukla A. Diagnosis of breast cancer by modular evolutionary neural networks. Int J Biomed Eng Technol. 2011;7(2):194–211.

    Article  Google Scholar 

  18. Kononenko Y. Machine learning for medical diagnosis: history, state of the art and perspective. Artif Intell Med. 2001;23(1):89–109.

    Article  CAS  PubMed  Google Scholar 

  19. Lee WP, Lin CH. Combining expression data and knowledge ontology for gene clustering and network reconstruction. Cogn Comput. 2016;8(2):217–27.

    Article  Google Scholar 

  20. Liu P, Tang G. Multi-criteria group decision-making based on interval neutrosophic uncertain linguistic variables and choquet integral. Cogn Comput. 2016;8(6):1036–56.

    Article  Google Scholar 

  21. Mahdavi MM. Implementation of a recommender system on medical recognition and treatment. Int J e-Education, e-Business, e-Management e-Learning. 2012;2(4):315–18.

    Google Scholar 

  22. Moein S, Monadjemi SA, Moallem P. A novel fuzzy-neural based medical diagnosis system. Int J Biol Med Sci. 2009;4(3):146–50.

    Google Scholar 

  23. Mondal K, Pramanik S. Neutrosophic tangent similarity measure and its application to multiple attribute decision making. Neutrosophic Sets and Systems. 2015;9:85–92.

  24. Sahin R. Neutrosophic hierarchical clustering algorithms. Neutrosophic Sets and Systems. 2014;2:18–24.

  25. Samuel AE, Balamurugan M. Fuzzy max–min composition technique in medical diagnosis. Appl Math Sci. 2012;6(35):1741–46.

    Google Scholar 

  26. Shinoj TK, John SJ. Intuitionistic fuzzy multi sets and its application in medical diagnosis. World Acad Sci Eng Technol. 2012;6:1418–21.

    Google Scholar 

  27. Smarandache F. A unifying field in logics. Neutrosophy: neutrosophic probability set and logic. Rehoboth: American Research Press; 1998.

    Google Scholar 

  28. Szmidt E, Kacprzyk J. Intuitionistic fuzzy sets in some medical applications. In: Proceeding of computational intelligence: theory and applications; 2001. p. 148–51.

  29. Szmidt E, Kacprzyk J. An intuitionistic fuzzy set based approach to intelligent data analysis: an application to medical diagnosis. In: Proceeding of recent advances in intelligent paradigms an applications; 2003. p. 57–70.

  30. Szmidt E, Kacprzyk J. A similarity measure for intuitionistic fuzzy sets and its application in supporting medical diagnostic reasoning. In: Proceeding of artificial intelligence and soft computing; 2004. p. 388–93.

  31. Son LH, Thong NT. Intuitionistic fuzzy recommender systems: an effective tool for medical diagnosis. Knowl-Based Syst 2015;74:133–50.

    Article  Google Scholar 

  32. Son LH, Tuan TM. A cooperative semi-supervised fuzzy clustering framework for dental X-ray image segmentation. Expert Syst Appl 2016;46:380–93.

    Article  Google Scholar 

  33. Thong NT, Son LH. HIFCF: an effective hybrid model between picture fuzzy clustering and intuitionistic fuzzy recommender systems for medical diagnosis. Expert Syst Appl. 2015;42(7):3682–701.

    Article  Google Scholar 

  34. Tuan TM, Duc NT, Hai PV, Son LH. Dental diagnosis from x-ray images using fuzzy rule-based systems. International Journal of Fuzzy System Applications 2017;6(1):1–16.

    Article  Google Scholar 

  35. Tuan TM, Ngan TT, Son LH. A novel semi-supervised fuzzy clustering method based on interactive fuzzy satisficing for dental X-ray image segmentation. Appl Intell. 2016;45(2):402– 28.

    Article  Google Scholar 

  36. Tuan TM, Son LH. A novel framework using graph-based clustering for dental x-ray image search in medical diagnosis. International Journal of Engineering and Technology 2016;8(6):422– 27.

    Article  Google Scholar 

  37. Vendramin L, Campello RJ, Hruschka ER. Relative clustering validity criteria: a comparative overview. Statistical analysis and data mining: the ASA data science journal 2010;3(4):209– 35.

    Google Scholar 

  38. Yao Y. Three-way decisions and cognitive computing. Cogn Comput 2016;8(4):543–54.

    Article  Google Scholar 

  39. Ye J. Improved cosine similarity measures of simplified neutrosophic sets for medical diagnoses. Artif Intell Med 2007;63:171–79.

    Article  Google Scholar 

  40. Ye S, Ye J. Dice similarity measure between single valued neutrosophic multisets and its application in medical diagnosis. Neutrosophic Sets and Systems 2014;6:48–53.

    Google Scholar 

  41. Ye J. A netting method for clustering-simplified neutrosophic information. J Intell Syst 2014;23(4):379–89.

    Google Scholar 

  42. Ye S, Fu J, Ye J. Medical diagnosis using distance-based similarity measures of single valued neutrosophic multisets. Neutrosophic Sets and Systems 2015;07:47–54.

    Google Scholar 

  43. Ye J, Fu J. Multi-period medical diagnosis method using a single valued neutrosophic similarity measure based on tangent function. Comput Methods Prog Biomed 2015;123:142–49.

    Article  Google Scholar 

  44. Ye J. Clustering methods using distance-based similarity measures of single-valued neutrosophic sets. J Intell Syst 2014;23(4):379–89.

    Google Scholar 

  45. Yu J, Rui Y, Tang YY, Tao D. High-order distance-based multiview stochastic learning in image classification. IEEE Transactions on Cybernetics 2014;44(12):2431–42.

    Article  PubMed  Google Scholar 

  46. Yu J, Zhang B, Kuang Z, Lin D, Fan J. 2016. iPrivacy: image privacy protection by identifying sensitive objects via deep multi-task learning. IEEE Trans Inf Forensics Secur, doi:10.1109/TIFS.2016.2636090.

  47. Yu J, Yang X, Gao F, Tao D. 2017. Deep multimodal distance metric learning using click constraints for image ranking. IEEE Transactions on Cybernetics. doi:10.1109/TCYB.2016.2591583.

  48. Zhang M, Zhang L, Cheng H. Segmentation of ultrasound breast images based on a neutrosophic method. Opt Eng 2010;49(11):117001. doi:10.1117/1.3505.

    Article  Google Scholar 

  49. Zhang HY, Ji P, Wang JQ, Chen XH. A neutrosophic normal cloud and its application in decision-making. Cogn Comput 2016;8(4):649–69.

    Article  CAS  Google Scholar 

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Correspondence to Le Hoang Son.

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Thanh, N.D., Ali, M. & Son, L.H. A Novel Clustering Algorithm in a Neutrosophic Recommender System for Medical Diagnosis. Cogn Comput 9, 526–544 (2017). https://doi.org/10.1007/s12559-017-9462-8

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