Cross-Network Clustering and Cluster Ranking for Medical Diagnosis

• Published in: 2017 IEEE 33rd International Conference on Data Engineering (ICDE) pp. 163 - 166
• Main Authors: Jingchao Ni, Hongliang Fei, Wei Fan, Xiang Zhang
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.04.2017
• Subjects: Bipartite graph; Clustering algorithms; Diseases; Linear programming; Medical diagnosis; Medical diagnostic imaging; Stochastic processes
• ISSN: 2375-026X
• DOI: 10.1109/ICDE.2017.65

Automating medical diagnosis is an important data mining problem, which is to infer likely disease(s) for some observed symptoms. Algorithms to the problem are very beneficial as a supplement to a real diagnosis. Existing diagnosis methods typically perform the inference on a sparse bipartite graph with two sets of nodes representing diseases and symptoms, respectively. By using this graph, existing methods basically assume no direct dependency exists between diseases (or symptoms), which may not be true in reality. To address this limitation, in this paper, we introduce two domain networks encoding similarities between diseases and those between symptoms to avoid information loss as well as to alleviate the sparsity problem of the bipartite graph. Based on the domain networks and the bipartite graph bridging them, we develop a novel algorithm, CCCR, to perform diagnosis by ranking symptom-disease clusters. Comparing with existing approaches, CCCR is more accurate, and more interpretable since its results deliver rich information about how the inferred diseases are categorized. Experimental results on real-life datasets demonstrate the effectiveness of the proposed method.