Identification of cancer rules in Viet Nam by network modularity

Minh Tan Nguyen; Duc Tinh Pham; Viet Ha Tran; Dzung Tien Tran

doi:10.15625/2525-2518/16678

Author affiliations

Authors

Minh Tan Nguyen Center of Information –Library, Hanoi University of Industry, 298 Cau Dien Street, Bac Tu Liem District, Ha Noi, Viet Nam
Duc Tinh Pham Graduate University of Science and Technology, Vietnam Academy of Science and Technology, 18 Hoang Quoc Viet, Cau Giay, Ha Noi, Viet Nam
Viet Ha Tran Department of SoftwareEngineering, Faculty of Information Technology, Hanoi University of Industry, 298 Cau Dien Street, Bac Tu Liem District, Ha Noi, Viet Nam
Dzung Tien Tran Department of Software Engineering, Faculty of Information Technology, Hanoi University of Industry http://orcid.org/0000-0002-7966-6838

DOI:

https://doi.org/10.15625/2525-2518/16678

Keywords:

network modularity, cancer rule identification, network inference, graph mining

Abstract

Data clustering tools can uncover new knowledge to be used in cancer diagnosis and treatment. In this study, we proposed a novel method to cluster records of a relation. First, we designed an algorithm that calculates the similarity between record pairs of the relation, and then this similarity measure was used to generate a network corresponding to the relation. Finally, we used a Network science technique to detect clusters of records from the network and extract insights from the clusters. Applying the method to mine a cancer-screening dataset at the Vietnam Central Cancer Hospital with over 177,000 records, we have discovered several new cancer laws in Viet Nam, which contribute to cancer detection and treatment support. It is disclosed from these cancer rules that some types of cancer run in certain family lines and living places in Viet Nam. Clustering a relation by Network science approach can be a good choice for mining large-scale relational data.

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