Abstract
There is always an increasing demand for the development of new soft computing technologies for medical diagnosis in regular clinical use. With the advent of soft computing technologies, the use of intelligent methods and algorithms provides a viable alternative for vague, uncertain and complex real life problems such as diagnosis of diseases, for which mathematical model is not available. In this work, a hybrid artificial intelligence system namely Adaptive Neuro-Fuzzy Inference System (ANFIS) based model is developed for early diagnosis of dengue disease. Dengue fever, caused by the dengue virus is an infectious tropical disease. Dengue disease has been considered as a fatal disease and delay in diagnosis may increase its severity as well as life risk of the patients. The signs and symptoms of early dengue disease are nonspecific and overlap with the other infectious diseases. So, the principal aim of this study was to develop an acceptable diagnostic system for early diagnosis of dengue disease.
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Acknowledgements
The authors thank Dr. B.K. Bezbaruah, the Superintendent of Guwahati Medical College and Hospital, Guwahati, Assam, who helped a lot to collect patients’ data, which helped us in the development of the dengue diagnostic system.
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Saikia, D., Dutta, J.C. (2017). Adaptive Network Based Fuzzy Inference System for Early Diagnosis of Dengue Disease. In: Bhatia, S., Mishra, K., Tiwari, S., Singh, V. (eds) Advances in Computer and Computational Sciences. Advances in Intelligent Systems and Computing, vol 553. Springer, Singapore. https://doi.org/10.1007/978-981-10-3770-2_68
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DOI: https://doi.org/10.1007/978-981-10-3770-2_68
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