Breast Cancer Comorbidities Network Analysis: Break the Code with Optimum Community Detection Algorithms

Abstract

Breast cancer is a prominent global health concern, as the data from the International Agency for Research on Cancer (IARC) shows that breast cancer is the leading cancer type with new cases in 2020 and among the Top 5 cancer types with the most deaths. This research aims to discover the breast cancer comorbidity diseases community with community detection algorithms, evaluate them based on modularity and similarity, suggest the best semantic similarity measurement and the optimal threshold value, and validate the data of breast cancer comorbidities with several data from research papers. The Wang algorithm, with a threshold value of 0.5, is chosen to build the network. Leiden, Louvain, RBER Pots, RB Pots, and Walktrap are the best five community detection algorithms. Similarity measurements with the best three fitness functions (edges inside, scaled density, and size) suggest that the Leiden-Louvain algorithm and RBER Pots-RB Pots algorithm are two pairs of algorithms with similar results. Other similarity measurements with the V-measure heatmap suggest that Louvain-Leiden (0.99), RB Pots-Leiden (0.97), and RB Pots-RBER Pots (0.96) results are similar. Comorbidity is then evaluated using the best five community detection algorithms and four centrality algorithms. As a result, fourteen diseases are agreed upon by the best five community detection algorithms, five diseases are agreed upon by four algorithms, two diseases are agreed upon by three algorithms, a disease is agreed upon by two algorithms, and ten diseases are agreed upon by an algorithm.