Title of article :
association analysis of obesity/overweight and breast cancer using data mining techniques
Author/Authors :
dehghani soufi, mahsa tabriz university of medical sciences - school of management and medical informatics - department of health information technology, tabriz, iran , ferdousi, reza tabriz university of medical sciences - school of management and medical informatics - department of health information technology, tabriz, iran
From page :
1
To page :
8
Abstract :
introduction: growing evidence has shown that some overweight factors could be implicated in tumor genesis, higher recurrence and mortality. in addition, association of various overweight factors and breast cancer has not been extensively explored. the goal of this research was to explore and evaluate the association of various overweight/obesity factors and breast cancer, based on obesity breast cancer data set. material and methods: several studies show that a significantly stronger association is obvious between overweight and higher breast cancer incidence, but the role of some overweight factors such as bmi, insulinresistance, homeostasis model assessment (homa), leptin, adiponectin, glucose and mcp.1 is still debatable, so for experiment of research work several clinical and biochemical overweight factors, including age, body mass index (bmi), glucose, insulin, homeostatic model assessment (homa), leptin, adiponectin, resistin and monocyte chemo attractant protein1(mcp1) were analyzed. data mining algorithms including kmeans, apriori, hierarchical clustering algorithm (hcm) were applied using orange version 3.22 as an open source data mining tool. results: the apriori algorithm generated a list of frequent item sets and some strong rules from dataset and found that insulin, homa and leptin are two items often simultaneously were seen for bc patients that leads to cancer progression. kmeans algorithm applied and it divided samples on three clusters and its results showed that the pair of lt;adiponectin, mcp.1 gt;  has the highest effect on seperation of clusters. in addition hcm was carried out and classified bc patients into 132 clusters to so this research apply hcm algorithm. we carried out hierarchical clustering with average linkage without purning and classified bc patients into 1–32 clusters in order to identify bc patients with similar charestrictics. conclusion: these finding provide the employed algorithms in this study can be helpful to our aim.
Keywords :
breast cancer , overweight , obesity , k , means , apriori
Journal title :
frontiers in health informatics
Journal title :
frontiers in health informatics
Record number :
2704997
Link To Document :
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