TY - JOUR ID - 4322 TI - A Fuzzy Expert System for Distinguishing between Bacterial and Aseptic Meningitis JO - Iranian Journal of Medical Physics JA - IJMP LA - en SN - AU - Langarizadeh, Mostafa AU - Khajehpour, Esmat AU - Khajehpour, Hassan AU - Farokhnia, Mehrdad AU - Eftekhari, Mahdi AD - Medical Informatics Dept., Faculty of Health Management and Information Sciences, Iran University of Medical Sciences, Tehran, Iran AD - Medical Informatics Dept., Tehran University of Medical Sciences, Tehran, Iran AD - Biomedical Engineering Dept., Tehran University of Medical Sciences, Tehran, Iran AD - Infectious Diseases Specialist, Kerman University of Medical Sciences, Kerman, Iran AD - Artificial Intelligence, Department of Computer Engineering, Shahid Bahonar University, Kerman, Iran Y1 - 2015 PY - 2015 VL - 12 IS - 1 SP - 1 EP - 6 KW - Aseptic meningitis KW - Bacterial meningitis KW - Expert system KW - Fuzzy Logic KW - Meningitis DO - 10.22038/ijmp.2015.4322 N2 - 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. UR - https://ijmp.mums.ac.ir/article_4322.html L1 - https://ijmp.mums.ac.ir/article_4322_e68f6bf153a99f5455869cbdc80086d3.pdf ER -