EXPERT FUZZY SYSTEMS FOR EVALUATION OF INTENSITY OF REACTIVE EDEMA OF SOFT TISSUES IN PATIENTS WITH DIABETES

The paper analyzes the main areas of application of mathematical methods in medical diagnostics, formulates principles of diagnostics based on fuzzy logic; developed mathematical models and algorithms that formalize the process of making diagnostic decisions based on fuzzy logic with quantitative and qualitative parameters of the patient's condition; developed mathematical models of membership function. Mathematical models and algorithms have been developed that formalize the process of making diagnostic decisions based on fuzzy logic with quantitative and qualitative parameters of the patient's condition; developed mathematical models of membership functions, formalizing the representation of quantitative and qualitative parameters of the patient's condition in the form of fuzzy sets, used in models and algorithms for diagnosis and finding a diagnosis of assessing the intensity of reactive postoperative edema in patients of all study groups. An expert system was implemented for solving the problems of medical diagnosis based on fuzzy logic when assessing the intensity of reactive swelling of soft tissues, which develops in the postoperative period in patients of all study groups against the background of diabetes. The paper analyzes the main areas of application of mathematical methods in medical diagnostics, formulates the principles of diagnostics based on fuzzy logic.

Keywords:

information expert system, control, method of fuzzy sets, medical diagnostics, diabetes, dentistry

References

Abdikerimova G. B. et al.: The analysis of textural images on the basis of orthogonal transformations. Journal of Theoretical and Applied Information Technologythis link is disabled 97(1), 2019, 15–22.

Arakeri G. et al.: A randomized clinical trial to compare the efficacy of submucosal Aprotinin injection and intravenous Dexamethasone in reducing pain and swelling after third molar surgery: A prospective study. Journal of Maxillofacial and Oral Surgery 12(1), 2013, 71–75. DOI: https://doi.org/10.1007/s12663-012-0364-8

Azarova A., Zhytkevych O.: Mathematical methods of identification of ukrainian enterprises competitiveness level by fuzzy logic using. Economic Annals-XXI 9–10(2), 2013, 59–62.

Clinical guidelines for the management of pulpal diseases, approved by Decree 15 of the Council of Public Association “Russian Dental Association”. Dated August 2, 2018.

Nizhynska-Astapenko Z. et al.: Information medical fuzzy-expert system for the assessment of the diabetic ketoacidosis severity on the base of the blood gases indices. Proc. SPIE 12126, 2021, 1212626. DOI: https://doi.org/10.1117/12.2616675

Rotshtein A.: Design and Tuning of Fussy IF – THEN Vuly for Medical Didicol Diagnosis. Fuzzy and Neuro-Fuzzy Systems in Medicine. CRC-Press, 1998, 235–295.

Serkova V. K. et al.: Medical expert system for assessment of coronary heart disease destabilization based on the analysis of the level of soluble vascular adhesion molecules. Proc. SPIE 10445, 2017, 104453O. DOI: https://doi.org/10.1117/12.2280984

Taubayev G. et al.: Machine learning algorithms and classification of textures. Journal of Theoretical and Applied Information Technologythis 98(23), 2020, 3854–3866.

Vasilevskyi O. et al.: Method of evaluating the level of confidence based on metrological risks for determining the coverage factor in the concept of uncertainty. Proc. SPIE 10808, 2018, 108082C.

Vassilenko V. et al.: Automated features analysis of patients with spinal diseases using medical thermal images. Proc. SPIE 11456, 2020, 114560L. DOI: https://doi.org/10.1117/12.2569780

Vavilova T. P. et al.: Dental pulp responsiveness. Moscow State University of Medicine and Dentistry, 2017.

Volgin M. A. et al.: Сomparative analysis of IL-1. COX-2 and type II collagenase gene expression profile in acute inflammed pulps. Endodontics today 4, 2016, 16–20.

Wójcik W. et al.: Information Technology in Medical Diagnostics II. Taylor & Francis Group. CRC Press, Balkema Book. London 2019. DOI: https://doi.org/10.1201/9780429057618

Wójcik W. et al.: Information Technology in Medical Diagnostics. CRC Press. 2017. DOI: https://doi.org/10.1201/9781315098050

Shkilniak, L., Wójcik, W., Pavlov, S., Vlasenko, O., Kanishyna, T., Khomyuk, I., … Iskakova, A. (2022). EXPERT FUZZY SYSTEMS FOR EVALUATION OF INTENSITY OF REACTIVE EDEMA OF SOFT TISSUES IN PATIENTS WITH DIABETES . Informatyka, Automatyka, Pomiary W Gospodarce I Ochronie Środowiska, 12(3), 59–63. https://doi.org/10.35784/iapgos.3037

EXPERT FUZZY SYSTEMS FOR EVALUATION OF INTENSITY OF REACTIVE EDEMA OF SOFT TISSUES IN PATIENTS WITH DIABETES

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