Assessment of Differential Diagnosis of Bacterial Meningitis from other Types of Meningitis Using Fuzzy Logic and Neural Networks
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 pu...
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| Format: | Article |
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Tehran University of Medical Sciences
2017-01-01
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| Series: | پیاورد سلامت |
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| Online Access: | http://payavard.tums.ac.ir/article-1-6121-en.html |
| _version_ | 1818983325349445632 |
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| author | Mostafa Langarizadeh Esmat Khajehpour Rahele Salari Hassan Khajehpour |
| author_facet | Mostafa Langarizadeh Esmat Khajehpour Rahele Salari Hassan Khajehpour |
| author_sort | Mostafa Langarizadeh |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-20T18:01:19Z |
| format | Article |
| id | doaj.art-4645951bab2343f7a7bfd086e9a1c997 |
| institution | Directory Open Access Journal |
| issn | 1735-8132 2008-2665 |
| language | fas |
| last_indexed | 2024-12-20T18:01:19Z |
| publishDate | 2017-01-01 |
| publisher | Tehran University of Medical Sciences |
| record_format | Article |
| series | پیاورد سلامت |
| spelling | doaj.art-4645951bab2343f7a7bfd086e9a1c9972022-12-21T19:30:37ZfasTehran University of Medical Sciencesپیاورد سلامت1735-81322008-26652017-01-01105453460Assessment of Differential Diagnosis of Bacterial Meningitis from other Types of Meningitis Using Fuzzy Logic and Neural NetworksMostafa Langarizadeh0Esmat Khajehpour1Rahele Salari2Hassan Khajehpour3 Assistant Professor, Health Information Management, School of Health Management and Information Sciences, Iran University of Medical Sciences, Tehran, Iran Master of Science in Medical Informatics, Vice Chancellery of Clinical Affairs, Rafsanjan University of Medical Sciences, Rafsanjan, Iran Ph.D. Student in Medical Informatics, School of Allied Medical Sciences, Tehran University of Medical Sciences, Tehran, Iran Ph.D. Student in Medical Engineering, School of Medicine, Tehran University of Medical Sciences, Tehran, Iran 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.http://payavard.tums.ac.ir/article-1-6121-en.htmlfuzzy logicneural networkbacterial meningitis |
| spellingShingle | Mostafa Langarizadeh Esmat Khajehpour Rahele Salari Hassan Khajehpour Assessment of Differential Diagnosis of Bacterial Meningitis from other Types of Meningitis Using Fuzzy Logic and Neural Networks پیاورد سلامت fuzzy logic neural network bacterial meningitis |
| title | Assessment of Differential Diagnosis of Bacterial Meningitis from other Types of Meningitis Using Fuzzy Logic and Neural Networks |
| title_full | Assessment of Differential Diagnosis of Bacterial Meningitis from other Types of Meningitis Using Fuzzy Logic and Neural Networks |
| title_fullStr | Assessment of Differential Diagnosis of Bacterial Meningitis from other Types of Meningitis Using Fuzzy Logic and Neural Networks |
| title_full_unstemmed | Assessment of Differential Diagnosis of Bacterial Meningitis from other Types of Meningitis Using Fuzzy Logic and Neural Networks |
| title_short | Assessment of Differential Diagnosis of Bacterial Meningitis from other Types of Meningitis Using Fuzzy Logic and Neural Networks |
| title_sort | assessment of differential diagnosis of bacterial meningitis from other types of meningitis using fuzzy logic and neural networks |
| topic | fuzzy logic neural network bacterial meningitis |
| url | http://payavard.tums.ac.ir/article-1-6121-en.html |
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