[Home ] [Archive]   [ فارسی ]  
:: Main :: About :: Current Issue :: Archive :: Search :: Submit :: Contact ::
:: Volume 1, Issue 1 (Fall 2014) ::
2014, 1(1): 19-25 Back to browse issues page
Fuzzy Expert System for Diagnosis of Bacterial Meningitis from Other Types of Meningitis in Children
Mostafa Langarizade , Esmat Khajehpour , Hassan Khajehpour , Tayebe Noori
MSc of Medical Informatics, Vice Chancellery of clinical affairs of Rafsanjan University of Medical Sciences, Rafsanjan, Iran
Abstract:   (5303 Views)

Introduction: Bacterial meningitis requires timely diagnosis and treatment otherwise it will have relatively high complications and mortality and morbidity. In the early stages of the disease distinguishing between bacterial meningitis that it is most dangerous type and other type is so complicated and inaccurate. Hence in this study a fuzzy expert system for distinguish bacterial meningitis from other kind of meningitis in children is presented.
Method: In the proposed fuzzy system, two fuzzy inference engines (The diagnosis of bacterial meningitis and the proposed new LP) were used. Mamdani model was used in both fuzzy inference engines using Max-Min as AND-OR operators and Centroid method was used as defuzzification technique.
 Results: The first fuzzy inference engine was evaluated using data obtained from 106 patients’ records admitted with meningitis. Accuracy, sensitivity, and precision of the system in terms of bacterial meningitis diagnosis were 91%, 100% and 89% respectively. The ROC curve was used to show system performance graphically and the area under the ROC curve was 0.947. To measure agreement of system results with the physician diagnosis, Kappa statistics was employed and showed a high relation (K=0.79, P<0.001). Extracted data from 75 cases with non-bacterial meningitis were used to evaluate the second inference engine and accuracy, sensitivity, and precision of this system were 96%, 100%, and 95% respectively, and the area under the ROC curve was 0.96 and Kappa statistic showed a very high agreement between the system output with physician diagnosis (K=0.87,P<0/001).
Conclusion: According to the complexity and importance of early diagnosis of bacterial meningitis, and favorable results of the implementation and evaluation of the suggested expert system, therefore this system can be useful for detecting and differentiating acute bacterial meningitis of other meningitis, but more studies must be performed for better assessment and verification of system.

Keywords: Bacterial meningitis, Expert system, Fuzzy logic, Children
eprint link: http://eprints.kmu.ac.ir/id/eprint/24249
Full-Text [PDF 603 kb]   (1707 Downloads)    
Type of Study: Original Article | Subject: Special
Received: 2014/11/11 | Accepted: 2014/12/8
Send email to the article author

Add your comments about this article
Your username or Email:

Write the security code in the box >

XML   Persian Abstract   Print

Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:

Langarizade M, Khajehpour E, Khajehpour H, Noori T. Fuzzy Expert System for Diagnosis of Bacterial Meningitis from Other Types of Meningitis in Children. Journal of Health and Biomedical Informatics. 2014; 1 (1) :19-25
URL: http://jhbmi.ir/article-1-66-en.html

Volume 1, Issue 1 (Fall 2014) Back to browse issues page
مجله انفورماتیک سلامت و زیست پزشکی Journal of Health and Biomedical Informatics
Persian site map - English site map - Created in 0.06 seconds with 31 queries by YEKTAWEB 3657