A fuzzy rule-based expert system for diagnosing cystic fibrosis
Keywords:
Expert systems, Fuzzy logic, Cystic fibrosisAbstract
Background: Finding a valid diagnosis is mostly a prolonged process. Current advances in the sector of artificial intelligence have led to the appearance of expert systems that enrich the experiences and capabilities of doctors for making decisions for their patients. Objective: The objective of this research was developing a fuzzy expert system for diagnosing Cystic Fibrosis (CF). Methods: Defining the risk factors and then, designing the fuzzy expert system for diagnosis of CF were carried out in this cross-sectional study. To evaluate the performance of the proposed system, a dataset that corresponded to 70 patients with respiratory disease who were serially admitted to the CF Clinic in the Pediatric Respiratory Diseases Center, Masih Daneshvari Hospital in Tehran, Iran during August 2016 to January 2017 was considered. Whole procedures of system construction were implemented in a MATLAB environment. Results: Results showed that the suggested system can be used as a strong diagnostic tool with 93.02% precision, 89.29% specificity, 95.24% sensitivity and 92.86% accuracy for diagnosing CF. There was also a good relationship between the user and the system through the appealing user interface. Conclusion: The system is equipped with information, knowledge, and expertise from certified specialists; hence, as a training tool it can be useful for new physicians. It is worth mentioning that the accomplishment of this project depends on advocacy of decision making in CF diagnosis. Nevertheless, it is expected that the system will reduce the number of false positives and false negatives in unusual cases.
References
Josefiok M, Sauer J. Towards an Expert System for the Field of Neurology Based on Fuzzy Logic. Joint
German/Austrian Conference on Artificial Intelligence (Künstliche Intelligenz). 2015; 333-40. doi:
1007/978-3-319-24489-1_31.
Kunhimangalam R, Ovallath S, Joseph PK. A novel fuzzy expert system for the identification of severity of
carpal tunnel syndrome. BioMed research international. 2013; 2013. doi: 10.1155/2013/846780.
Elahi E, Khodadad A, Kupershmidt I, Ghasemi F, Alinasab B, Naghizadeh R, et al. A haplotype framework
for cystic fibrosis mutations in Iran. J Mol Diagn. 2006; 8(1): 119-27. doi: 10.2353/jmoldx.2006.050063.
PMID: 16436643, PMCID: PMC1867567.
Singh M, Rebordosa C, Bernholz J, Sharma N. Epidemiology and genetics of cystic fibrosis in Asia: In
preparation for the next‐generation treatments. Respirology. 2015; 20(8): 1172-81. doi:
1111/resp.12656. PMID: 26437683.
Farjadian S, Moghtaderi M, Kashef S, Alyasin S, Najib K, Saki F. Clinical and genetic features in patients
with cystic fibrosis in southwestern Iran. Iran J Pediatr. 2013; 23(2): 212-5. PMID: 23724185, PMCID:
PMC3663315.
Baghaie N, Kalilzadeh S, Hassanzad M, Parsanejad N, Velayati A. Determination of mortality from cystic
fibrosis. Pneumologia. 2010; 59(3): 170-3. PMID: 21053647.
Kahkouee S, Namini AK, Boloursaz MR. Quantitative evaluation of high-resolution CT findings in advanced
cystic fibrosis patients based on the Brody scoring. Journal of Comprehensive Pediatrics. 2014; 5(1). doi:
17795/compreped-4901.
Mehdizadeh Hakkak A, Keramatipour M, Talebi S, Brook A, Tavakol Afshari J, Raazi A, et al. Analysis of
CFTR gene mutations in children with cystic fibrosis, first report from North-East of Iran. Iran J Basic Med
Sci. 2013; 16(8): 917-21. PMID: 24106596, PMCID: PMC3786104.
Khalilzadeh S, Boloursaz M, Baghaie N, Fard EH, Hassanzad M, Emami H. Microbial colonization and drug
resistance in patients with cystic fibrosis. Journal of Comprehensive Pediatrics. 2012; 3(1): 25-8. doi:
17795/compreped-6944.
Khalilzadeh S, Hassanzad M, Baghaie N, Parsanejad N, Boloursaz MR, Fahimi F. Shwachman score in
clinical evaluation of cystic fibrosis. Journal of Comprehensive Pediatrics. 2013; 4(1): 82-5. doi:
17795/compreped-4558.
Khalilzadeh S, Kahkouee S, Hassanzad M, Parsanejad N, Baghaie N, Bloorsa MR. The correlation of brody
high resolution computed tomography scoring system with clinical status and pulmonary function test in
patients with cystic fibrosis. Iran J Med Sci. 2011; 36(1): 18-23. PMID: 23365473, PMCID: PMC3559112.
Vasheghani Farahani F, Zarandi MF, Ahmadi A. Fuzzy rule based expert system for diagnosis of lung cancer.
Fuzzy Information Processing Society (NAFIPS) held jointly with 2015 5th World Conference on Soft
Computing, 2015 Annual Conference of the North American; 2015: IEEE. doi: 10.1109/NAFIPS- WConSC.2015.7284206.
Alipour J, Safari Lafti S, Askari Majdabadi H, Yazdiyani A, Valinejadi A. Factors affecting hospital
information system acceptance by caregivers of educational hospitals based on technology acceptance model
(TAM): A study in Iran. IIOAB Journal. 2016; 119-23.
Norouzi J, Yadollahpour A, Mirbagheri SA, Mazdeh MM, Hosseini SA. Predicting renal failure progression
in chronic kidney disease using integrated intelligent fuzzy expert system. Computational and mathematical
methods in medicine. 2016; 2016. doi: 10.1155/2016/6080814.
Reis MA, Ortega NR, Silveira PS. Fuzzy expert system in the prediction of neonatal resuscitation. Braz J
Med Biol Res. 2004; 37(5): 755-64. doi: 10.1590/S0100-879X2004000500018. PMID: 15107939.
Neshat M, Yaghobi M, Naghibi M, Esmaelzadeh A. Fuzzy expert system design for diagnosis of liver
disorders. Knowledge Acquisition and Modeling, 2008 KAM'08 International Symposium on; 2008: IEEE.
doi: 10.1109/KAM.2008.43.
Zarandi MF, Zarinbal M, Izadi M. Systematic image processing for diagnosing brain tumors: A Type-II fuzzy
expert system approach. Applied soft computing. 2011; 11(1): 285-94. doi: 10.1016/j.asoc.2009.11.019.
Nobile L, Cosenza B, Amato M, Guarnotta V, Giordano C, Galluzzo A, et al. Development of a fuzzy expert
system for the control of glycemia in type 1 diabetic patients. Comput Aided Chem Eng. 2011; 29: 1568-72.
doi: 10.1016/B978-0-444-54298-4.50092-1.
Mago VK, Mago A, Sharma P, Mago J. Fuzzy logic based expert system for the treatment of mobile tooth.
Adv Exp Med Biol. 2011; 696: 607-14. doi: 10.1007/978-1-4419-7046-6_62. PMID: 21431602.
Keles A, Keles A, Yavuz U. Expert system based on neural-fuzzy rules for thyroid diseases diagnosis.
Computer Applications for Bio-technology, Multimedia, and Ubiquitous City. 2012; 94-105.
Ephzibah E, Sundarapandian V. A Fuzzy Rule Based Expert System for Effective Heart Disease Diagnosis.
Advances in Computer Science and Information Technology Computer Science and Engineering. 2012: 196- 203. doi: 10.1007/978-3-642-27308-7_20.
Domínguez Hernández KR, Aguilar Lasserre AA, Posada Gómez R, Palet Guzmán JA, González Sánchez
BE. Development of an expert system as a diagnostic support of cervical cancer in atypical glandular cells,
based on fuzzy logics and image interpretation. Computational and mathematical methods in medicine. 2013.
doi: 10.1155/2013/796387. PMID: 23690881, PMCID: PMC3652118.
Zarandi MF, Damirchi-Darasi SR, Izadi M, Turksen I, Ghahazi MA. Fuzzy rule based expert system to
diagnose spinal cord disorders. Norbert Wiener in the 21st Century (21CW), 2014 IEEE Conference on;
: IEEE. doi: 10.1109/NORBERT.2014.6893897.
Ghahazi MA, Zarandi MF, Harirchian M, Damirchi-Darasi SR. Fuzzy rule based expert system for diagnosis
of multiple sclerosis. Norbert Wiener in the 21st Century (21CW), 2014 IEEE Conference on; 2014: IEEE.
doi: 10.1109/NORBERT.2014.6893855.
Zarinbal M, Zarandi MF, Turksen IB, Izadi M. A type-2 fuzzy image processing expert system for diagnosing
brain tumors. J Med Syst. 2015; 39(10): 110. doi: 10.1007/s10916-015-0311-6. PMID: 26276018.
Chakraborty C, Mitra T, Mukherjee A, Ray AK. CAIDSA: Computer-aided intelligent diagnostic system for
bronchial asthma. Expert Systems with Applications. 2009; 36(3): 4958-66. doi:
1016/j.eswa.2008.06.025.
Zarandi MF, Zolnoori M, Moin M, Heidarnejad H. A fuzzy rule-based expert system for diagnosing asthma.
Scientia Iranica Transaction E, Industrial Engineering. 2010; 17(2): 129.
Zolnoori M, Zarandi MHF, Moin M, Taherian M. Fuzzy rule-based expert system for evaluating level of
asthma control. J Med Syst. 2012; 36(5): 2947-58. doi: 10.1007/s10916-011-9773-3. PMID: 21912973.
Lomotan EA, Hoeksema LJ, Edmonds DE, Ramírez-Garnica G, Shiffman RN, Horwitz LI. Evaluating the
use of a computerized clinical decision support system for asthma by pediatric pulmonologists. Int J Med
Inform. 2012; 81(3): 157-65. doi: 10.1016/j.ijmedinf.2011.11.004. PMID: 22204897, PMCID:
PMC3279612.
Avci E. A new expert system for diagnosis of lung cancer: GDA—LS_SVM. J Med Syst. 2012; 36(3): 2005- 9. doi: 10.1007/s10916-011-9660-y. PMID: 21340704.
Zolnoori M, Zarandi MH, Moin M. Application of intelligent systems in asthma disease: designing a fuzzy
rule-based system for evaluating level of asthma exacerbation. J Med Syst. 2012; 36(4): 2071-83. doi:
1007/s10916-011-9671-8. PMID: 21399914.
Dexheimer JW, Abramo TJ, Arnold DH, Johnson KB, Shyr Y, Ye F, et al. An asthma management system
in a pediatric emergency department. Int J Med Inform. 2013; 82(4): 230-8. doi:
1016/j.ijmedinf.2012.11.006. PMID: 23218449, PMCID: PMC3646328.
Zolnoori M, Zarandi MH, Moin M, Teimorian S. Fuzzy rule-based expert system for assessment severity of
asthma. J Med Syst. 2012; 36(3): 1707-17. doi: 10.1007/s10916-010-9631-8. PMID: 21128097.
Zolnoori M, Jones JF, Moin M, Heidarnejad H, Fazlollahi MR, Hosseini M. Evaluation of user interface of
computer application developed for screening pediatric asthma. International Conference on Universal
Access in Human-Computer Interaction. 2013; 563-70. doi: 10.1007/978-3-642-39194-1_65.
Anand SK, Kalpana R, Vijayalakshmi S. Design and implementation of a fuzzy expert system for detecting
and estimating the level of asthma and chronic obstructive pulmonary disease. Middle-East Journal of
Scientific Research. 2013; 14(11): 1435-44. doi: 10.5829/idosi.wasj.2013.23.02.13046.
Vasheghani Farahani F, Ahmadi A, Zarandi MF. Lung nodule diagnosis from CT images based on ensemble
learning. Computational Intelligence in Bioinformatics and Computational Biology (CIBCB), 2015 IEEE
Conference on; 2015: IEEE.
Efosa IC, Akwukwuma V. Knowledge-based fuzzy inference system for sepsis diagnosis. International
Journal of Computational Science and Information Technology (IJCSITY). 2013; 1(3): 1-7. doi:
5121/ijcsity.2013.1301.
Garibaldi JM. Fuzzy expert systems. Do Smart Adaptive Systems Exist? 2005; 105-32. doi: 10.1007/3-540- 32374-0_6.
Nascimento LF, Rocha Rizol PM, Abiuzi LB. Establishing the risk of neonatal mortality using a fuzzy
predictive model. Cad Saude Publica. 2009; 25(9): 2043-52. doi: 10.1590/S0102-311X2009000900018.
PMID: 19750391.
Adeli A, Neshat M. A fuzzy expert system for heart disease diagnosis. Proceedings of International Multi
Conference of Engineers and Computer Scientists Hong Kong: IMECS Conference Proceedings; 2010.
Wang L-X. A course in fuzzy systems: Prentice-Hall press, USA; 1999.
Yang L, Neagu D, Cronin MT, Hewitt M, Enoch SJ, Madden JC, et al. Towards a fuzzy expert system on
toxicological data quality assessment. Molecular Informatics. 2013; 32(1): 65-78. doi:
1002/minf.201200082. PMID: 27481024.
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