Fuzzy models in descriptive and predictive analysis of medical data of patients with chronic non-communicable diseases
https://doi.org/10.25881/18110193_2025_4_28
Abstract
As life expectancy increases, the number of people, including those of working age, with chronic noncommunicable diseases (CNCDs), increasing the workload of healthcare personnel, is growing. This increases the need for automated processing of medical data, particularly data from monitoring systems for key health indicators of patients with CNCDs. Key tasks in this area include accurately describing the patient's current health status and timely diagnosis. Addressing these challenges is crucial for the effective management of patients with CNCDs, supporting physicians in choosing the optimal treatment strategy. Fuzzy models offer significant potential for solving these problems due to the ambiguity of medical data, the ability to model the knowledge of medical professionals, and their low computational complexity. This article reviews, systematizes, and summarizes the results of 29 studies published between 2015 and 2025 on the descriptive and predictive analysis of numerical medical data from patients with CNDs using fuzzy models employing fuzzy sets and fuzzy logical inference. Particular attention is paid to assessing the accuracy of fuzzy models for various CNDs. The analysis of publications demonstrates the competitiveness and high efficiency of fuzzy models in data analysis, as evidenced by accuracy (from 90% to 99.61%) and sensitivity (from 80.94% to 98.57%) metrics, with the exception of cancer studies. The obtained results can serve as a basis for the development of medical decision support systems.
About the Author
T. V. Afanaseva
Plekhanov Russian University of Economics
Russian Federation
Russian Federation
DSc, Associate Professor
Moscow
References
1. Hussain A, Wenbi R, Xiaosong Z, Hongyang W, da Silva AL. Personal home healthcare system for the cardiac patient of smart city using fuzzy logic. J Adv Inf Technol. 2016; 7(1): 58-64. doi: 10.12720/jait.7.1.58-64.
2. Santos M, Munoz R, Olivares R, et al. Online heart monitoring systems on the internet of health things environments: A survey, a reference model and an outlook. Inf. Fusion. 2020; 53: 222-239.
3. «Strategiya tsifrovoy transformatsii otrasli «Zdravookhranenie» do 2024 goda i na planovyy period do 2030 goda» : Rasporiazhenie Pravitel'stva Rossiyskoy Federatsii ot 29 dekabria 2021 g. № 3980-r. (In Russ.)
4. Zadeh LA. Fuzzy sets. Inf Cont. 1965; 8(3): 338-353.
5. Mamdani EH. Application of fuzzy logic to approximate reasoning using linguistic systems. Fuzz Sets Syst. 1977; 26: 1182-1191.
6. Takagi T, Sugeno M. Fuzzy identification of systems and its applications to modeling and control. IEEE Transactions on Systems, Man, and Cybernetics: Systems. 1985; 15(1): 116-132.
7. Kobrinskii BA. Fuzzy and reflection in the construction of a medical expert system. J Soft Eng Appl. 2020; 2(13): 15-23.
8. Germashev IV, Dubovskaya VI. Primenenie modeley nechetkoy matematiki dlya resheniya zadach meditsinskoy diagnostiki. Mathematical Physics and Computer Simulation. 2021;24(4):53–66. (In Russ.)
9. Aamir KM, Sarfraz L, Ramzan M, Bilal M, Shafi J, Attique M. A fuzzy rule-based system for classification of diabetes. Sensors. 2021; 21(23): 8095.
10. Colella Y, et al. A clinical decision support system based on fuzzy rules and classification algorithms for monitoring the physiological parameters of type-2 diabetic patients. Math Biosci Eng. 2021; 18: 2654-2674.
11. Aggarwal A, Chakradar M, Bhatia MS, Kumar M, Stephan T, et al. COVID-19 risk prediction for diabetic patients using fuzzy inference system and machine learning approaches. J Healthc Eng. 2022; 1: 4096950. doi: 10.1155/2022/4096950.
12. Al-Dmour JA, Sagahyroon A, Al-Ali AR, Abusnana S. A fuzzy logic-based warning system for patients classification. Health Inf J. 2019; 25(3): 1004-1024. doi: 10.1177/1460458217735674.
13. El-Sappagh S, et al. An ontology-based interpretable fuzzy decision support system for diabetes diagnosis. IEEE Access. 2018; 6: 37371-37394.
14. Shoaip N, El-Sappagh S, Barakat S. Ontology enhanced fuzzy clinical decision support system. U-Healthcare Monit Syst. 2019; 1: 147-177. doi: 10.1016/B978-0-12-815370-3.00007-4.
15. Mohd Sharif NA, Ahmad, N, et al. A fuzzy rule-based expert system for asthma severity identification in emergency department. J Inf Commun Technol. 2019; 18(4): 415-438.
16. Badnjević A, Pokvić LG, Deumić A, et al. Diagnostic of asthma using fuzzy rules implemented in accordance with international guidelines and physician's experience. In: Proceedings of the 2016 39th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO); May 30-June 3, 2016; Opatija, Croatia. IEEE; 2016: 375-380.
17. Sharma R, Jain SK, Kumar S, Sami P. A study for the diagnosis of asthma disease using fuzzy logic based system. Int J Health Sci. 2022; 6(S1): 12156-12165. doi: 10.53730/ijhs.v6nS1.8041.
18. Yin Z, Li H, Han X, Ran Y, Wang Z, Dong Z. Clinical decision support system using hierarchical fuzzy diagnosis model for migraine and tension-type headache based on International Classification of Headache Disorders, 3rd edition. Front Neurol. 2024; 15: 1444197. doi: 10.3389/fneur.2024.1444197.
19. Taherkhani N, Sepehri MM, Khasha R, Shafaghi S. Ranking patients on the kidney transplant waiting list based on fuzzy inference system. BMC Nephrol. 2022; 23(1). doi: 10.1186/s12882-022-02662-5.
20. Adnan AN, Zakaria NI, Abdullah NSK, Ya'acob S. Fuzzy logic: an application to detect chronic kidney disease and failure. Open Int J Inform. 2019; 7(S2): 118-126.
21. Singla J, Kaur B, Prashar D, Jha S, Joshi GP, Park K, et al. A novel fuzzy logic-based medical expert system for diagnosis of chronic kidney disease. Mobile Inform Syst. 2020; 2020: 1-13. doi: 10.1155/2020/8887627.
22. Scrobotă I, Băciuț G, Filip AG, Todor B, Blaga F, Băciuț MF. Application of Fuzzy Logic in Oral Cancer Risk Assessment. Iran J Public Health. 2017; 46(5): 612-619.
23. Rawat M, Pathak P, Vat P. An approach to diagnosis of prostate cancer using fuzzy logic. Int J Reconfig Embed Syst. 2024; 13(1): 192-200.
24. Khalil AM, Li S.-G, Lin Y, Li, H.-X, Ma S.-G. A new expert system in prediction of lung cancer disease based on fuzzy soft sets. Soft Comput. 2020; 24: 14179-14207.
25. Nguyen T, Khosravi A, Creighton D, Nahavandi S. Medical data classification using interval type-2 fuzzy logic system and wavelets. Appl Soft Comput. 2015; 30: 812-822. doi: 10.1016/j.asoc.2015.02.016.
26. Kora P, et al. Detection of cardiac arrhythmia using fuzzy logic. Informatics Med Unlocked. 2019; 17: 100257.
27. Bykov AV, Korenevskiy NA, Rodionova SN, Tsymbal EV. Metod i nechetkaya model' otsenki dinamiki razvitiya kriticheskoy ishemii nizhnikh konechnostey. Vestnik novykh meditsinskikh tekhnologiy. 2018; 25(4): 251-257. (In Russ.) doi: 10.24411/1609-2163-2018-16227.
28. Afanasieva TV, Rodionova TE. Methodology of patient-oriented assessment of cardiovascular health of men using fuzzy sets and formal conceptual analysis. In: Developments of Artificial Intelligence Technologies in Computation and Robotics. World Scientific Proceedings Series on Computer Engineering and Information Science. 2020: 857-865.
29. Afanasieva T. Information granulating and its use in descriptive and predictive analysis of medical data. In: Lecture Notes in Networks and Systems. Vol 566. Springer. 2023: 229-238.
30. Priyatharshini R, Chitrakala S. A self-learning fuzzy rule-based system for risk-level assessment of coronary heart disease. IETE J Res. 2018; 65(3): 288-297. doi: 10.1080/03772063.2018.1431062.
31. Shojaei E, Luque D, Rexachs A, Wong A, Epelde F. Evaluation of lifestyle effects on chronic disease management. In: Proceedings of the 2018 Winter Simulation Conference (WSC); December 9-12, 2018; Gothenburg, Sweden. IEEE; 2018:1037-1048. doi: 10.1109/WSC.2018.8632508.
32. Kasbe T, Pippal RS. Design of heart disease diagnosis system using fuzzy logic. In: Proceedings of the 2017 International Conference on Energy, Communication, Data Analytics and Soft Computing (ICECDS); August 1-2, 2017; Chennai, India. IEEE; 2017:3183-3187.
33. Kahtan H, Zamli KZ, Ashikin WN, Fatthi WA, Abdullah A, Shahaiyusniezam N. Heart disease diagnosis using fuzzy logic. In: Proceedings of the International Conference on Software and Computer Applications; February 8-10, 2018; Kuantan, Malaysia. IEEE; 2018:297-301. doi: 10.1109/ICSCA.2018.8327237.
34. El-Ibrahimi A, Ahmad H, Al-Ashwal FY, et al. Fuzzy based system for coronary artery disease prediction using subtractive clustering and risk factors data. Intell Based Med. 2025; 11: 100208.
35. Marateb HR, Goudarzi S. A noninvasive method for coronary artery diseases diagnosis using a clinically-interpretable fuzzy rule-based system. J Res Med Sci. 2015; 20(3): 214-223.
36. Mahmoodabadi Z, Abadeh MS. CADICA: Diagnosis of Coronary Artery Disease Using the Imperialist Competitive Algorithm. J Comput Sci Eng. 2014; 8(2): 87-93. doi: 10.5626/JCSE.2014.8.2.87.
Review
For citations:
Afanaseva T.V. Fuzzy models in descriptive and predictive analysis of medical data of patients with chronic non-communicable diseases. Medical Doctor and Information Technologies. 2025;(4):28-43. (In Russ.) https://doi.org/10.25881/18110193_2025_4_28
Views:
32
Email this article 