Document Type : Original Paper
Medical Informatics Dept., Faculty of Health Management and Information Sciences, Iran University of Medical Sciences, Tehran, Iran
Medical Informatics Dept., Tehran University of Medical Sciences, Tehran, Iran
Biomedical Engineering Dept., Tehran University of Medical Sciences, Tehran, Iran
Infectious Diseases Specialist, Kerman University of Medical Sciences, Kerman, Iran
Artificial Intelligence, Department of Computer Engineering, Shahid Bahonar University, Kerman, Iran
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.
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).
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.