Hyperbox clustering with Ant Colony Optimization (HACO) method and its application to medical risk profile recognition

Authors:
G. N. Ramos;Y. Hatakeyama;F. Dong;K. Hirota
Affiliations:
Department of Computational Intelligence and Systems Science, Tokyo Institute of Technology, Yokohama, Japan;Center of Medical Information Science, Medical School, Kochi University, Nankoku city, Japan;Department of Computational Intelligence and Systems Science, Tokyo Institute of Technology, Yokohama, Japan;Department of Computational Intelligence and Systems Science, Tokyo Institute of Technology, Yokohama, Japan
Venue:
Applied Soft Computing
Year:
2009

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Abstract

A clustering method, called HACO (Hyperbox clustering with Ant Colony Optimization), is proposed for classifying unlabeled data using hyperboxes and an ant colony meta-heuristic. It acknowledges the topological information (inherently associated to classification) of the data while looking in a small search space, providing results with high precision in a short time. It is validated using artificial 2D data sets and then applied to a real medical data set, automatically extracting medical risk profiles, a laborious operation for doctors. Clustering results show an improvement of 36% in accuracy and 7 times faster processing time when compared to the usual ant colony optimization approach. It can be further extended to hyperbox shape optimization (fine tune accuracy), automatic parameter setting (improve usability), and applied to diagnosis decision support systems.