Case Base Reasoning (CBR) and Density Based Spatial Clustering Application with Noise (DBSCAN)-based Indexing in Medical Expert Systems

Abstract

Case-based Reasoning (CBR) has been widely applied in the medical expert systems. CBR has computational time constraints if there are too many old cases on the case base. Cluster analysis can be used as an indexing method to speed up searching in the case retrieval process. This paper propose retrieval method using Density Based Spatial Clustering Application with Noise (DBSCAN) for indexing and cosine similarity for the relevant cluster searching process. Three medical test data, that are malnutrition disease data, heart disease data and thyroid disease data, are used to measure the performance of the proposed method. Comparative tests conducted between DBSCAN and Self-organizing maps (SOM) for the indexing method, as well as between Manhattan distance similarity, Euclidean distance similarity and Minkowski distance similarity for calculating the similarity of cases. The result of testing on malnutrition and heart disease data shows that CBR with cluster-indexing has better accuracy and shorter processing time than non-indexing CBR. In the case of thyroid disease, CBR with cluster-indexing has a better average retrieval time, but the accuracy of non-indexing CBR is better than cluster indexing CBR. Compared to SOM algorithm, DBSCAN algorithm produces better accuracy and faster process to perform clustering and retrieval. Meanwhile, of the three methods of similarity, the Minkowski distance method produces the highest accuracy at the threshold ≥ 90.

Keywords

• case-base reasoning
• clustering
• indexing
• som
• dbscan