Abstract
A fuzzy rule based expert system STRESSDIAG is presented for diagnosis of stress types including positive and negative rules. After designing and building a suitable inference engine for this system, to create effective knowledge base consisting of more than 700 positive rules for confirmation of conclusion and of more than 100 negative rules for exclusion of the same conclusion. How the rule base is constructed, managed and used are focused on for diagnosis of diagnosis of stress types such as light stress, middle stress, serious stress and serious stress with mental disorder. The inference engine shows how to combine positive and negative rules. The first evaluation of STRESSDIAG is presented by the medical expert’s group in the field of mental diseases in Vietnam and confirmed that STRESSDIAG diagnoses with a high accuracy.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Phuong, N.H.: Towards Intelligent Systems for Integrated Western and Eastern Medicine. The Gioi Publishers, Hanoi (1997)
Shortliffe, E.H.: Computer Based Medical Consultation: MYCIN. Am. Elsevier, New York (1976)
Adlassnig, K.-P.: CADIAG-2: computer – assisted medical diagnosis using fuzzy subsets. In: Gupta, M.M., Sanchez, E. (eds.) Approximate Reasoning in Decision Analysis, pp. 219–247. North-Holland Publishing Company, Amsterdam (1982)
Daniel, M., Hajek, P., Phuong, N.H.: CADIAG-2 and MYCIN-like systems. Int. J. Artif. Intell. Med. 9, 241–259 (1997)
Kandel, A.: Fuzzy Expert Systems. CRC Press, Boca Raton (2000)
Shortliffe, E., Buchanan, B., Feigenbaum, E.: Knowledge engineering for medical decision making: a review of computer-based clinical decision aids. In: Proceedings of IEEE, vol. 69, p. 1207 (1997)
Giaratano, J., Riley, G.: Expert Systems: Principles and Programming. PWS Publishing Company (1994)
Miller, R.A., Pople, H.E., Myers, J.D.: INTERNIST-1, an experimental computer-based diagnostic consultant for general internal medicine. New Engl. J. Med. 307(8), 468–476 (1982)
Zadeh, L.A.: Fuzzy sets. Inf. Control 8, 338–353 (1965)
Zadeh, L.A.: The role of fuzzy logic in the management of uncertainty in expert systems. Fuzzy Sets Syst. 11, 199 (1983)
Phuong, N.H.: Fuzzy set theory and medical expert systems. survey and model. In: Proceedings of SOFSEM 1995. Theory and Practice in Informatics. LNCS, vol. 1012, pp. 431–436. Springer, Heidelberg (1995)
Phuong, N.H., Kreinovich, V.: Fuzzy logic and its applications in medicine. Int. J. Med. Inform. 62, 165–173 (2001)
Hajek, P., Havranek, T., Jirousek, R.: Uncertain Information Processing in Expert Systems. CRC Press, Boca Raton (1992)
Nu, M.T., Phuong, N.H., Hirota, K.: Modeling a fuzzy rule based expert system combining positive and negative knowledge for medical consultations using the importance of symptoms. In: Proceedings of IFSA-SCIS 2017, 27–30 June 2017, Otsu, Japan (2017)
ICD - 10, Medical Publisher, Vietnam (2010). (in Vietnamese and in English)
http://giadinh.net.vn/y-te/30-nguoi-viet-nam-bi-roi-loan-tam-than2018.htm
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Nu, M.T., Phuong, N.H., Dung, H.T. (2019). STRESSDIAG: A Fuzzy Expert System for Diagnosis of Stress Types Including Positive and Negative Rules. In: Kearfott, R., Batyrshin, I., Reformat, M., Ceberio, M., Kreinovich, V. (eds) Fuzzy Techniques: Theory and Applications. IFSA/NAFIPS 2019 2019. Advances in Intelligent Systems and Computing, vol 1000. Springer, Cham. https://doi.org/10.1007/978-3-030-21920-8_34
Download citation
DOI: https://doi.org/10.1007/978-3-030-21920-8_34
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-21919-2
Online ISBN: 978-3-030-21920-8
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)