Abstract
The functioning of the medical diagnostic process is very similar to the pattern of the case-based reasoning cycle (CBR). This resemblance has prompted several research groups to build on the CBR, which is a paradigm of problem solving based on past experiences, in the design of medical decision support systems. In this article, we propose a medical decision support system specifically in dermatology based on fuzzy logic to predict the response of a patient with plantar and common warts to immunotherapy treatment. The aim of this work is to improve the retrieval step, which is a very important phase in the CBR cycle, by incorporating segmentation techniques “fuzzy clustering”. The proposed approach is composed of two parts; the part of the clustering by the Fuzzy C-Means algorithm and the part of case-based reasoning realized by the JColibri platform. The use of the FCM is to reduce the search space and solve the problem of rapid retrieval of similar cases.
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Saadi, F., Atmani, B., Henni, F. (2020). Integration of Fuzzy Clustering into the Case Base Reasoning for the Prediction of Response to Immunotherapy Treatment. In: Djeddi, C., Jamil, A., Siddiqi, I. (eds) Pattern Recognition and Artificial Intelligence. MedPRAI 2019. Communications in Computer and Information Science, vol 1144. Springer, Cham. https://doi.org/10.1007/978-3-030-37548-5_15
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