Volume 10, Issue 5 (1-2017)                   payavard 2017, 10(5): 453-460 | Back to browse issues page

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Langarizadeh M, Khajehpour E, Salari R, Khajehpour H. Assessment of Differential Diagnosis of Bacterial Meningitis from other Types of Meningitis Using Fuzzy Logic and Neural Networks. payavard. 2017; 10 (5) :453-460
URL: http://payavard.tums.ac.ir/article-1-6121-en.html
1- Assistant Professor, Health Information Management, School of Health Management and Information Sciences, Iran University of Medical Sciences, Tehran, Iran
2- Master of Science in Medical Informatics, Vice Chancellery of Clinical Affairs, Rafsanjan University of Medical Sciences, Rafsanjan, Iran , e_khajehpoor@yahoo.com
3- Ph.D. Student in Medical Informatics, School of Allied Medical Sciences, Tehran University of Medical Sciences, Tehran, Iran
4- Ph.D. Student in Medical Engineering, School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
Abstract:   (5674 Views)

Background and Aim: Bacterial meningitis detection is a complicated problem because of having several components in order to be diagnosed and distinguished from other types of meningitis. Fuzzy logic and neural network, frequently used in expert systems, are able to distinguish such diseases. The purpose of this paper is to compare Fuzzy logic and artificial neural networks for distinguishing bacterial meningitis from other types of meningitis.
Materials and Methods: In this study to detect and distinguish bacterial meningitis from other types of meningitis, in the first step 6 attributes were selected by infectious disease specialists. In the second step, systems were designed by Matlab software. The systems were evaluated by 26 records of meningitis patients, and results were analyzed by SPSS software.
Results: The evaluation showed that the accuracy, specificity and sensitivity of fuzzy method were 88%, 92% and 100% respectively and those of neural network methods were 92%, 94% and 88% respectively. The Kappa test result in fuzzy and neural network methods were 0.83 (p<0.001) and 0.83 (p<0.001). The areas under the ROC curves were 0.94 and 0.91 respectively.
Conclusion: The sensitivity, the Kappa test results and the areas under the ROC curve of the fuzzy logic method were better than neural network method. However the fuzzy logic method is more reliable to distinguish bacterial meningitis from other type of Meningitis, the evaluation result were obtained from 26 records of meningitis patient which were hospitalized in the same center leads to the study be still open.

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Type of Study: Original Research | Subject: Health Information Technology
ePublished: 1399/07/23

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