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Matinfar, Farzam, Tavakoli Golpaygani, Ali. (1401). A Fuzzy Expert System for Early Diagnosis of Multiple Sclerosis. سامانه مدیریت نشریات علمی, 12(2), 181-188. doi: 10.31661/jbpe.v0i0.1236
Farzam Matinfar; Ali Tavakoli Golpaygani. "A Fuzzy Expert System for Early Diagnosis of Multiple Sclerosis". سامانه مدیریت نشریات علمی, 12, 2, 1401, 181-188. doi: 10.31661/jbpe.v0i0.1236
Matinfar, Farzam, Tavakoli Golpaygani, Ali. (1401). 'A Fuzzy Expert System for Early Diagnosis of Multiple Sclerosis', سامانه مدیریت نشریات علمی, 12(2), pp. 181-188. doi: 10.31661/jbpe.v0i0.1236
Matinfar, Farzam, Tavakoli Golpaygani, Ali. A Fuzzy Expert System for Early Diagnosis of Multiple Sclerosis. سامانه مدیریت نشریات علمی, 1401; 12(2): 181-188. doi: 10.31661/jbpe.v0i0.1236

A Fuzzy Expert System for Early Diagnosis of Multiple Sclerosis

Journal of Biomedical Physics and Engineering
مقاله 9، دوره 12، شماره 2، خرداد و تیر 2022، صفحه 181-188    اصل مقاله (867.86 K)
نوع مقاله: Original Research
شناسه دیجیتال (DOI): 10.31661/jbpe.v0i0.1236
نویسندگان
Farzam Matinfar* 1؛ Ali Tavakoli Golpaygani2
1PhD, Department of Statistics, Mathematics, and Computer Science, Allameh Tabataba’i University, Iran
2PhD, Department of Biomedical Engineering, Standard Research Institute, Karaj, Iran
چکیده
Background: Artificial intelligence plays an important role in medicine. Specially, expert systems can be designed for diagnosis of disease.
Objective: Artificial intelligence can be used for diagnosis of disease. This study proposes an expert system for diagnosis of Multiple Sclerosis based on clinical symptoms and demographic characteristics. Specially, it recommends patients to refer to a specialist for further investigation.
Material and Methods: In this empirical study, some symptoms of Multiple Sclerosis are mapped to fuzzy sets. Moreover, several rules are defined for prediction of Multiple Sclerosis. The fuzzy sets and rules form the knowledge base of the expert system. Patients enter their symptoms and demographic information via a user interface and Mamdani method is used in inference engine to produce the appropriate recommendation.
Results: The precision, recall, and F-measure are used as criteria to analyze the efficiency of the expert system. The results show that the designed expert system can recommend patients for further investigation as effective as specialists. Specially, while the proposed expert system recommended referring to a doctor for some healthy users, most of the MS patients are diagnosed.
Conclusion: The proposed expert system in this study can analyze the symptoms of patients to predict the Multiple Sclerosis disease. Therefore, it can investigate initial status of patients in a rapid and cost-effective manner. Moreover, this system can be applied in situations and places, which human experts are unavailable.
کلیدواژه‌ها
Neurology؛ Multiple Sclerosis؛ Diagnosis؛ Expert Systems؛ Fuzzy Logic
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مراجع
  1. Torres A, Nieto JJ. Fuzzy logic in medicine and bioinformatics. Journal of Biomedicine & Biotechnology. 2006;2006(2):91908. doi: 10.1155/JBB/2006/91908. PubMed PMID: 16883057. PubMed PMCID: PMC1559939.
  2. Jackson P. Introduction to expert systems. United States: Publishing Co Inc; 1998.
  3. Sharma K, Virmani J. A decision support system for classification of normal and medical renal disease using ultrasound images: A decision support system for medical renal diseases. International Journal of Ambient Computing and Intelligence. 2017;8(2):52-69. doi: 10.4018/IJACI.2017040104.
  4. Morris D, Bharadwaj P. An Electronic Clinical Decision Support Tool to Assist Hospital Physicians with Prognosis: Development and Mixed Methods Evaluation (SA508A). Journal of Pain and Symptom Management. 2018;55(2):641-2. doi: 10.1016/j.jpainsymman.2017.12.181.
  5. Hassan N, Arbaiy N, Shah NA. Fuzzy expert system for heart attack diagnosis. Materials Science and Engineering. 2017;226(1):012111. doi: 10.1088/1757-899X/226/1/012111.
  6. Langarizadeh M, Khajehpour E, Khajehpour H, Farokhnia M, Eftekhari M. A fuzzy expert system for distinguishing between bacterial and aseptic meningitis. Iranian Journal of Medical Physics. 2015;12(1):1-6. doi: 10.22038/IJMP.2015.4322.
  7. Yilmaz A, Dagli M, Allahverdi N. A fuzzy expert system design for iron deficiency anemia. 7th International Conference on Application of Information and Communication Technologies (AICT); 2013. doi: 10.1109/ICAICT.2013.6722707.
  8. Zarandi MF, Soltanzadeh S, Mohammadi A, Castillo O. Designing a general type-2 fuzzy expert system for diagnosis of depression. Applied Soft Computing. 2019;80:329-41. doi: 10.1016/j.asoc.2019.03.027.
  9. Allahverdi N, Akcan T. A Fuzzy Expert System design for diagnosis of periodontal dental disease. 5th International Conference on Application of Information and Communication Technologies (AICT); Baku, Azerbaijan: IEEE; 2015. doi: 10.1109/ICAICT.2011.6110913.
  10. Esposito M, De Falco I, De Pietro G. An evolutionary-fuzzy DSS for assessing health status in multiple sclerosis disease. International Journal of Medical Informatics. 2011;80(12):e245-54. doi: 10.1016/j.ijmedinf.2011.09.003. PubMed PMID: 21963232.
  11. Mutawa AM, Alzuwawi MA. Multilayered rule-based expert system for diagnosing uveitis. Artificial Intelligence in Medicine. 2019;99:101691. doi: 10.1016/j.artmed.2019.06.007. PubMed PMID: 31606113.
  12. Pal D, Mandana KM, Pal S, Sarkar D, Chakraborty C. Fuzzy expert system approach for coronary artery disease screening using clinical parameters. Knowledge-Based Systems. 2012;36:162-74. doi: 10.1016/j.knosys.2012.06.013.
  13. Wagner C, Hagras H. Toward general type-2 fuzzy logic systems based on zSlices. IEEE Transactions on Fuzzy Systems. 2010;18(4):637-60. doi: 10.1109/TFUZZ.2010.2045386.
  14. Lublin FD, Reingold SC, Cohen JA, et al. Defining the clinical course of multiple sclerosis: the 2013 revisions. Neurology. 2014;83(3):278-86. doi: 10.1212/WNL.0000000000000560. PubMed PMID: 24871874. PubMed PMCID: PMC4117366.
  15. Brownlee WJ, Miller DH. Clinically isolated syndromes and the relationship to multiple sclerosis. Journal of Clinical Neuroscience. 2014;21(12):2065-71. doi: 10.1016/j.jocn.2014.02.026.
  16. Cree BA, Khan O, Bourdette D, Goodin DS, Cohen JA, Marrie RA, Glidden D, Weinstock-Guttman B, Reich D, Patterson N, Haines JL. Clinical characteristics of African Americans vs Caucasian Americans with multiple sclerosis. Neurology. 2004;63(11):2039-45. doi: 10.1212/01.wnl.0000145762.60562.5d. PubMed PMID: 15596747.
  17. Nazish S, Shahid R, Zafar A, Alshamrani F, Sulaiman AA, Alabdali M, Aljaafari D, Al Wabari E, Alkhamis FA. Clinical presentations and phenotypic spectrum of multiple sclerosis at a University Hospital in Saudi Arabia. J Clin Neurol. 2018;14(3):359-65. doi: 10.3988/jcn.2018.14.3.359. PubMed PMID: 29971975. PubMed PMCID: PMC6031989.
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