A Fuzzy Expert System for Distinguishing between Bacterial and Aseptic Meningitis
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 s...
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| Format: | Article |
| Language: | English |
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Mashhad University of Medical Sciences
2015-05-01
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| Series: | Iranian Journal of Medical Physics |
| Subjects: | |
| Online Access: | http://ijmp.mums.ac.ir/pdf_4322_e68f6bf153a99f5455869cbdc80086d3.html |
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| author | Mostafa Langarizadeh Esmat Khajehpour Hassan Khajehpour Mehrdad Farokhnia Mahdi Eftekhari |
| author_facet | Mostafa Langarizadeh Esmat Khajehpour Hassan Khajehpour Mehrdad Farokhnia Mahdi Eftekhari |
| author_sort | Mostafa Langarizadeh |
| collection | DOAJ |
| description | 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 |
| first_indexed | 2024-12-23T05:16:56Z |
| format | Article |
| id | doaj.art-9218a59844eb444ba345d9f03a5c33a6 |
| institution | Directory Open Access Journal |
| issn | 2252-0309 2345-3672 |
| language | English |
| last_indexed | 2024-12-23T05:16:56Z |
| publishDate | 2015-05-01 |
| publisher | Mashhad University of Medical Sciences |
| record_format | Article |
| series | Iranian Journal of Medical Physics |
| spelling | doaj.art-9218a59844eb444ba345d9f03a5c33a62022-12-21T17:58:49ZengMashhad University of Medical SciencesIranian Journal of Medical Physics2252-03092345-36722015-05-01121164322A Fuzzy Expert System for Distinguishing between Bacterial and Aseptic MeningitisMostafa Langarizadeh0Esmat Khajehpour1Hassan Khajehpour2Mehrdad Farokhnia3Mahdi Eftekhari4Medical Informatics Dept., Faculty of Health Management and Information Sciences, Iran University of Medical Sciences, Tehran, IranMedical Informatics Dept., Tehran University of Medical Sciences, Tehran, IranBiomedical Engineering Dept., Tehran University of Medical Sciences, Tehran, IranInfectious Diseases Specialist, Kerman University of Medical Sciences, Kerman, IranArtificial Intelligence, Department of Computer Engineering, Shahid Bahonar University, Kerman, IranIntroduction 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, Phttp://ijmp.mums.ac.ir/pdf_4322_e68f6bf153a99f5455869cbdc80086d3.htmlAseptic MeningitisBacterial MeningitisExpert SystemFuzzy LogicMeningitis |
| spellingShingle | Mostafa Langarizadeh Esmat Khajehpour Hassan Khajehpour Mehrdad Farokhnia Mahdi Eftekhari A Fuzzy Expert System for Distinguishing between Bacterial and Aseptic Meningitis Iranian Journal of Medical Physics Aseptic Meningitis Bacterial Meningitis Expert System Fuzzy Logic Meningitis |
| title | A Fuzzy Expert System for Distinguishing between Bacterial and Aseptic Meningitis |
| title_full | A Fuzzy Expert System for Distinguishing between Bacterial and Aseptic Meningitis |
| title_fullStr | A Fuzzy Expert System for Distinguishing between Bacterial and Aseptic Meningitis |
| title_full_unstemmed | A Fuzzy Expert System for Distinguishing between Bacterial and Aseptic Meningitis |
| title_short | A Fuzzy Expert System for Distinguishing between Bacterial and Aseptic Meningitis |
| title_sort | fuzzy expert system for distinguishing between bacterial and aseptic meningitis |
| topic | Aseptic Meningitis Bacterial Meningitis Expert System Fuzzy Logic Meningitis |
| url | http://ijmp.mums.ac.ir/pdf_4322_e68f6bf153a99f5455869cbdc80086d3.html |
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