Geodesic
Optimizing the decision-making process in fuzzy logic medical expert systems using historical data
Problemy fiziki, matematiki i tehniki, no. 1 (2019), pp. 78-84.

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Two of the most popular approaches to building decision support systems are fuzzy inference systems and supervised machine learning systems. However, fuzzy inference systems are based solely on expert decision-making process formalization and don’t take historical data into account, while machine learning systems infer certain statistical dependencies based solely on historical data, and those dependencies are very hard to formally analyze reason about from an expert point of view. The possible ways of combining these two approaches — building and optimizing medical fuzzy inference systems based on historical data are considered.
Keywords: expert systems, medical expert systems, fuzzy logic, machine learning. 
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A. V. Kurachkin; V. S. Sadau; O. M. Demidenko. Optimizing the decision-making process in fuzzy logic medical expert systems using historical data. Problemy fiziki, matematiki i tehniki, no. 1 (2019), pp. 78-84. http://geodesic.mathdoc.fr/item/PFMT_2019_1_a14/

[1] N.A. Kornevskiy, “Application of fuzzy logic for decision-making in medical expert systems”, Biomedical Engineering, 49:1 (2015), 46–49 | DOI

[2] L.C. Jain, A. Kandel, H.N.L. Teodorescu, Fuzzy and neuro-fuzzy systems in medicine, CRC Press, Boca Raton, Florida, USA, 2017, 393 pp. | MR

[3] D. Karaboga, E. Kaya, “Adaptive network based fuzzy inference system (ANFIS) training approaches: a comprehensive survey”, Artificial Intelligence Review, 49:1 (2018l), 1–31 | DOI

[4] O.V. Pribushenya, A.V. Kurochkin, “Otsenka platsentatsii pri mnogoplodnoi beremennosti s ispolzovaniem sovremennykh ekspertnykh kompyuternykh programm”, Sovremennye perinatalnye meditsinskie tekhnologii v reshenii problem demograficheskoi bezopasnosti, 10, Minsk, 2017, 106–111

[5] A.V. Kurochkin, V.S. Sadov, “Optimizatsiya parametrov ekspertnoi sistemy otsenki khorialnosti mnogoplodnykh beremennostei na osnove sistemy nechetkoi logiki po istoricheskim dannym”, Materialy mezhdunarodnoi nauchno-prakticheskoi konferentsii «Aktualnye voprosy sovremennoi nauki» (26 sentyabrya 2018 g.), Tomsk, 2018, 13–17