Abstract
Most patients have more than one disease, and these diseases are able to affect one another. In modern medicine, the etiology and pathophysiology of diseases are well known in detail. However, inter-disease relationships are still mysterious. Physicians’ knowledge and experience have great importance in such a multi-criteria case. Because medical doctors in internal medicine clinics deal with large numbers of patients with multiple diseases, they have quite a complex approach in treating illness. In this context, exposing the cause-and-effect relationships among diseases frequently seen in internal medicine will contribute to physicians’ ability to blend profound theoretical knowledge with experiential results. Therefore, this study presents a fuzzy DEMATEL (Decision-Making Trial-and-Evaluation Laboratory) method to assess the most common diseases in internal medicine outpatient clinics. The DEMATEL method allows one to identify and analyze significant diseases in internal medicine by considering the cause-and-effect relationship diagram. Likewise, fuzzy sets in DEMATEL overcome the uncertainty in making decisions about disease relationships and internal medicine experts’ judgments. When investigating the results, we have found dyspepsia, hyperlipidemia, and anemia to be crucial in terms of causes. When evaluating the effects, the most notable diseases are understood to be renal failure, malignancy, and hepatitis. The results indicate that in the presented study, we could successfully apply these methods to reveal the cause–effect of diseases. The results of this study will contribute to understanding the complex multi-criteria relationship among internal diseases using internists’ opinions.
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Acknowledgements
The authors would like to thank engineer Mr. Veysi Başhan (Research Assistant at Yildiz Technical University, Istanbul, Turkey) for sharing his profound knowledge about the application of the fuzzy DEMATEL method.
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Suzan, V., Yavuzer, H. A Fuzzy Dematel Method To Evaluate The Most Common Diseases In Internal Medicine. Int. J. Fuzzy Syst. 22, 2385–2395 (2020). https://doi.org/10.1007/s40815-020-00921-x
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DOI: https://doi.org/10.1007/s40815-020-00921-x