A Fuzzy Expert System for Distinguishing between Bacterial and Aseptic Meningitis

Langarizadeh, Mostafa and Khajehpour, Esmat and Khajehpour, Hassan and Farokhnia, Mehrdad and Eftekhari, Mahdi (2015) A Fuzzy Expert System for Distinguishing between Bacterial and Aseptic Meningitis. Iranian Journal of Medical Physics, 12 (1). pp. 1-6.

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Introduction Bacterial meningitis is a known infectious disease which occurs at early ages and should be promptly diagnosed and treated. Bacterial and aseptic meningitis are hard to be distinguished. Therefore, physicians should be highly informed and experienced in this area. The main aim of this study was to suggest a system for distinguishing between bacterial and aseptic meningitis, using fuzzy logic.   Materials and Methods In the first step, proper attributes were selected using Weka 3.6.7 software. Six attributes were selected using Attribute Evaluator, InfoGainAttributeEval, and Ranker search method items. Then, a fuzzy inference engine was designed using MATLAB software, based on Mamdani�s fuzzy logic method with max-min composition, prod-probor, and centroid defuzzification. The rule base consisted of eight rules, based on the experience of three specialists and information extracted from textbooks. Results Data were extracted from 106 records of patients with meningitis (42 cases with bacterial meningitis) in order to evaluate the proposed system. The system accuracy, specificity, and sensitivity were 89, 92 , and 97, respectively. The area under the ROC curve was 0.93, and Kappa test revealed a good level of agreement (k=0.84, P<0.0005). Conclusion According to the results, the suggested fuzzy system showed a good agreement and high efficiency in terms of distinguishing between bacterial and aseptic meningitis. To avoid unnecessary antibiotic treatments, patient hospitalization, and misdiagnosis of bacterial meningitis, such systems are useful and highly recommended. However, no system has been yet introduced with 100 correct output and further research is required to improve the results.

Item Type: Article
Subjects: WN Radiology . Diagnostic Imaging
Divisions: Journals > Iranian J Medical Physics
Depositing User: ijmp ijmp
Date Deposited: 24 Sep 2017 15:03
Last Modified: 24 Sep 2017 15:03
URI: http://eprints.mums.ac.ir/id/eprint/3454

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