The Diagnostic Value of Skin Disease Diagnosis Expert System
- PMID: 27046943
- PMCID: PMC4789723
- DOI: 10.5455/aim.2016.24.30-33
The Diagnostic Value of Skin Disease Diagnosis Expert System
Abstract
Background: Evaluation is a necessary measure to ensure the effectiveness and efficiency of all systems, including expert systems. The aim of this study was to determine the diagnostic value of expert system for diagnosis of complex skin diseases.
Methods: A case-control study was conducted in 2015 to determine the diagnostic value of an expert system. The study population included patients who were referred to Razi Specialized Hospital, affiliated to Tehran University of Medical Sciences. The control group was selected from patients without the selected skin diseases. Data collection tool was a checklist of clinical signs of diseases including pemphigus vulgaris, lichen planus, basal cell carcinoma, melanoma, and scabies. The sample size formula estimated 400 patients with skin diseases selected by experts and 200 patients without the selected skin diseases. Patient selection was undertaken with randomized stratified sampling and their sign and symptoms were logged into the system. Physician's diagnosis was determined as the gold standard and was compared with the diagnosis of expert system by SPSS software version 16 and STATA. Kappa statistics, indicators of sensitivity, specificity, accuracy and confidence intervals were calculated for each disease. An accuracy of 90% was considered appropriate.
Results: Comparing the results of expert system and physician's diagnosis at the evaluation stage showed an accuracy of 97.1%, sensitivity of 97.5% and specificity of 96.5% The Kappa test indicated a high agreement of 93.6%.
Conclusion: The expert system can diagnose complex skin diseases. Development of such systems is recommended to identify all skin diseases.
Keywords: Diagnostic value; Disease; Evaluation; Expert System; Skin.
Conflict of interest statement
References
-
- Abbasi MM, Kashiyarndi S. Clinical Decision Support Systems: A discussion on different methodologies used in Health Care. Marlaedalen University Sweden; 2006. [Accessed on: 25 Feb 2014]. Available at: http://www.idt.mdh.se/kurser/ct3340/ht10/FinalPapers/15-Abbasi Kashiyarn... .
-
- Zeki TS, Malakooti MV, Ataeipoor Y, Talayeh Tabibi S. An Expert System for Diabetes Diagnosis. American Academic & Scholarly Research Journal. 2012;4(5)
-
- Maghsoudi R, Bagheri A, Maghsoudi M. Diagnosis Prediction of Lichen Planus, Leukoplakia and Oral Squamous Cell Carcinoma by using an Intelligent System Based on Artificial Neural Networks. Journal of Dentomaxillofacial Radiology, Pathology and Surgery. 2013;2(2):1–8.
-
- Sadoughi F, Sheikhtaheri A. Applications of Artificial Intelligence in Clinical Decision-Making: Opportunities and Challenges. Health Information Management. 2011;8(3):445.
-
- Naser SSA, Akilla AN. A proposed Expert system for Skin Diseases Diagnosis. Journal of Applied Sciences Research. 2008;4(12):1682–93.
LinkOut - more resources
Full Text Sources
Other Literature Sources