Abstract
In this paper, we propose a 2n-valued fuzzy logic (2nvFL), which is especially suitable for representing linguistic terms, fuzzy concepts and qualitative reasoning and meanwhile avoids the difficulties of constructing fuzzy membership functions. More specifically, we present its syntax, semantics and minimal axiomatic system FL\(_0\), and prove some logical properties and soundness of it. In addition, we also compare the 2nvFL method with another fuzzy reasoning method in solving a healthcare problem. The results show that in the same data environment, our 2nvFL method can also complete fuzzy reasoning based on fuzzy numbers. Moreover, our 2nvFL’s method has several advantages: (1) Our 2nvFL method does not involve membership functions of fuzzy sets and avoids the difficulty of setting membership functions of fuzzy linguistic terms. (2) Our 2nvFL can be established on an axiomatic system, and its theorem derivation is sound. (3) Our 2nvFL method can be used for qualitative reasoning with heterogeneous data, so it has a great potential in a wide range of applications. (4) It is easy to flexibly determine the truth value set of our 2n-valued logic according to specific application environments and specific problems.
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References
G. Cosma, D. Brown, M. Archer, M. Khan, A.G. Pockley, A survey on computational intelligence approaches for predictive modeling in prostate cancer. Expert Syst. Appl. 70, 1–19 (2017)
G. Cosma, G. Acampora, D. Brown, R.C. Rees, M. Khan, A.G. Pockley, Prediction of pathological stage in patients with prostate cancer: A neuro-fuzzy model. PLoS ONE 11(6), 1–27 (2016)
M. Castanho, F. Hernandes, A. De Ré, S. Rautenberg, A. Billis. Fuzzy expert system for predicting pathological stage of prostate cancer. Expert Syst. Appl. 40(2), 466-470 (2013)
W. Froelich, E.I. Papageorgiou, M. Samarinas, K. Skriapas, Application of evolutionary fuzzy cognitive maps to the long-term prediction of prostate cancer. Appl. Soft Comput. 12(12), 3810–3817 (2012)
A. Karami, A. Gangopadhyay, B. Zhou, H. Kharrazi. Fuzzy approach topic discovery in health and medical corpora. Int, J. Fuzzy Syst. https://doi.org/10.1007/s40815-017-0327-9. Accessed 17 May 2017
M. Pota, E. Scalco, G. Sanguineti, A. Farneti, G.M. Cattaneo, G. Rizzo, Early prediction of radiotherapy-induced parotid shrinkage and toxicity based on CT radiomics and fuzzy classification. Artif. Intell. Med. 81, 41–53 (2017)
M. Nilashi, O. Ibrahim, H. Ahmadi, L. Shahmoradi, A knowledge-based system for breast cancer classification using fuzzy logic method. Telematics Inform. 34, 133–144 (2017)
T. Nguyen, A. Khosravi, D. Creighton, S. Nahavandi, Classification of healthcare data using genetic fuzzy logic system and wavelets. Expert Syst. Appl. 42, 2184–2197 (2015)
G.H.B. Miranda, J.C. Felipe, Computer-aided diagnosis system based on fuzzy logic for breast cancer categorization. Comput. Biol. Med. 64, 334–346 (2015)
J.S. de Bruin, K.-P. Adlassnig, A. Blacky, W. Koller, Detecting borderline infection in an automated monitoring system for healthcare-associated infection using fuzzy logic. Artif. Intell. Med. 69, 33–41 (2016)
A. Seitinger, A. Rappelsberger, H. Leitich, M. Binder, K.-P. Adlassnig, Executable medical guidelines with Arden Syntax—applications in dermatology and obstetrics. Artif. Intell. Med. (2016). https://doi.org/10.1016/j.artmed.2016.08.003
M.-H. Wang, C.-S. Lee, K.-L. Hsieh, C.-Y. Hsu, G. Acampora, C.-C. Chang, Ontology-based multi-agents for intelligent healthcare applications. J. Ambient Intell. Hum. Comput 1, 111–131 (2010)
M. Pota, E. Scalco, G. Sanguineti, A. Farneti, G.M. Cattaneo, G. Rizzo, M. Esposito, Early prediction of radiotherapy-induced parotid shrinkage and toxicity based on CT radionics and fuzzy classification. Artif. Intell. Med. 81, 41–53 (2017)
M. Roham, A.R. Gabrielyan, N.P. Archer, Predicting the impact of hospital health information technology adoption on patient satisfaction. Artif. Intell. Med. 56, 123–135 (2012)
S. Nazari, M. Fallah, H. Kazemipoor, A. Salehipour, A fuzzy inference-fuzzy analytic hierarchy process-based clinical decision support system for diagnosis of heart diseases. Expert Syst. Appl. 95, 261–271 (2018)
Acknowledgements
This work was supported by the National Natural Science Foundation of China (Nos. 61662007, 61762016, and 61762015) and Guangxi Key Lab of Multi-Source Information Mining and Security (No. 18-A-01-02).
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Liao, Y., Wu, J., Luo, X. (2019). A Multi-valued Fuzzy Logic for Qualitative Reasoning in Healthcare. In: Wu, C., Chyu, MC., Lloret, J., Li, X. (eds) Proceedings of the 2nd International Conference on Healthcare Science and Engineering . ICHSE 2018. Lecture Notes in Electrical Engineering, vol 536. Springer, Singapore. https://doi.org/10.1007/978-981-13-6837-0_17
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DOI: https://doi.org/10.1007/978-981-13-6837-0_17
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