Structured Case Base Knowledge using Unsupervised Learning

In medicine, medical knowledge representation and reasoning processes are the main issues of Medical Decision Support Systems, MDSS. This paper proposes an approach allowing to design a new representation of medical knowledge model using the Case-Based Reasoning approach and the Unsupervised Learning (K-Means clustering) method. The proposed approach is evaluated using the Stroke Knowledge Base. Obtained results indicate that applying the clustering approach on the case base and, then, using the K-Nearest Neighbor for each cluster is more efficient than the only use of the K-Nearest Neighbor without applying the clustering on the case base. The provided representation and the achieved results constitute an appropriate approach in order to reach several purposes of MDSS in terms of clustering, similarity estimation and quality assessment.