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| Case Based Medical Diagnosis of Occupational Chronic Lung Diseases From Their Symptoms and Signs
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Source |
International Journal of Biometrics and Bioinformatics (IJBB) |
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Table of Contents |
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Complete Issue PDF(1.69MB) |
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Volume: 5 Issue: 4 |
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Pages: NULL |
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Publication
Date: September / October 2011 |
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ISSN
(Online): 1985-2347 |
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216 - 224 |
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Published
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05-10-2011 |
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Publisher |
CSC
Journals, Kuala Lumpur,
Malaysia |
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ADDITIONAL
INFORMATION |
| Keywords Abstract References Cited by Related Articles Collaborative
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KEYWORDS: Clinical Support System, Artificial Intelligence, Case-Based Reasoning, Pathologist |
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| This Manuscript is indexed in the following databases/websites:- |
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| 1. Directory of Open Access Journals (DOAJ) |
| 2. Scribd |
| 3. Google Scholar |
| 4. Docstoc |
| 5. Bielefeld Academic Search Engine (BASE) |
| 6. iSEEK |
| 7. Academic Journals Database |
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| The clinical decision support system using the case based reasoning (CBR) methodology of Artificial Intelligence (AI) presents a foundation for a new technology of building intelligent computer aided diagnoses systems. This Technology directly addresses the problems found in the traditional Artificial Intelligence (AI) techniques, e.g. the problems of knowledge acquisition, remembering, robust and maintenance. In this paper, we have used the Case Based Reasoning methodology to develop a clinical decision support system prototype for supporting diagnosis of occupational lung diseases. 127 cases were collected for 14 occupational chronic lung diseases, which contains 26 symptoms. After removing the duplicated cases from the database, the system has trained set of 47 cases for Indian Lung patients. Statistical analysis has been done to determine the importance values of the case features. The retrieval strategy using nearest-neighbor approaches is investigated. The results indicate that the nearest neighbor approach has shown the encouraging outcome, used as retrieval strategy. A Consultant Pathologist’s interpretation was used to evaluate the system. Results for Sensitivity, Specificity, Positive Prediction Value and the Negative Prediction Value are 95.3%, 92.7%, 98.6% and 81.2% respectively. Thus, the result showed that the system is capable of assisting an inexperience pathologist in making accurate, consistent and timely diagnoses, also in the study of diagnostic protocol, education, self-assessment, and quality control. In this paper, clinical decision support system prototype is developed for supporting diagnosis of occupational lung diseases from their symptoms and signs through employing Microsoft Visual Basic .NET 2005 along with Microsoft SQL server 2005 environment with the advantage of Object Oriented Programming technology |
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Prem Pal Singh Tomar, Ranjit Singh, P K Saxena, B K Sharma, “A medical multimedia based DSS for heart diseases diagnosis and training”, Canadian Journal on Biomedical Engineering & Technology Vol. 3 No. 2, pp. 30-38, February 2012.
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yasni
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CORE (COnnecting REpositories)
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| Prempal Singh Tomar : Colleagues
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| Ranjit Singh : Colleagues
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| P K Saxena : Colleagues
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| Jeetu Sharma : Colleagues
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