• Logo
  • HamaraJournals

Association Analysis of Obesity/Overweight and Breast Cancer Using Data Mining Techniques

Mahsa Dehghani Soufi, Reza Ferdousi



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, insulin-resistance, 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 protein-1(MCP-1) were analyzed. Data mining algorithms including k-means, 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. K-means algorithm applied and it divided samples on three clusters and its results showed that the pair of <Adiponectin, MCP.1>  has the highest effect on seperation of clusters. In addition HCM was carried out and classified BC patients into 1-32 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.


Argolo DF, Hudis CA, Iyengar NM. The impact of obesity on breast cancer. Current Oncology Reports. 2018; 20(6): 47.

Kang C, LeRoith D, Gallagher EJ. Diabetes, obesity, and breast cancer. Endocrinology. 2018; 159(11): 3801-12. PMID: 30215698 DOI: 10.1210/en.2018-00574

Torre, LA, Islami, F, Siegel, RL, Ward, EM, Jemal, A. Global cancer in women: Burden and trends. Cancer Epidemiol Biomarkers Prev. 2017; 26(4): 444-57. PMID: 28223433 DOI: 10.1158/1055-9965.EPI-16-0858

American Institute for Cancer Research. Breast cancer [Internet]. 2015 [cited: 17 Aug 2019; updated: 9 Jan 2020]. Available from: http://www.aicr.org/continuous-update-project/breast-cancer.html.

Patrício M, Pereira J, Crisóstomo J, Matafome P, Gomes M, Seiça R, et al. Using resistin, glucose, age and BMI to predict the presence of breast cancer. BMC Cancer. 2018; 18(1): 29. PMID: 29301500 DOI: 10.1186/s12885-017-3877-1

Cohen DH, LeRoith D. Obesity, type 2 diabetes, and cancer: The insulin and IGF connection. Endocr Relat Cancer. 2012; 19(5): F27-45. PMID: 22593429 DOI: 10.1530/ERC-11-0374

Crisostomo J, Matafome P, Santos-Silva D, Gomes AL, Gomes M, Patrício M, et al. Hyperresistinemia and metabolic dysregulation: A risky crosstalk in obese breast cancer. Endocrine. 2016; 53(2): 433-42. PMID: 26892376 DOI: 10.1007/s12020-016-0893-x

Hossain R, Mahmud SH, Hossin MA, Noori SR, Jahan H. PRMT: Predicting risk factor of obesity among middle-aged people using data mining techniques. Procedia Computer Science. 2018; 132: 1068-76.

Yazgana P, Kusakci AO. A literature survey on association rule mining algorithms. Southeast Europe Journal of Soft Computing. 2016; 5(1): 5-14.

Dey A. Machine learning algorithms: A review. International Journal of Computer Science and Information Technologies. 2016; 7(3): 1174-9.

Aličković E, Subasi A. Breast cancer diagnosis using GA feature selection and Rotation Forest. Neural Computing and Applications. 2017; 28(4): 753-63.

Ghani MU, Alam TM, Jaskani FH. Comparison of classification models for early prediction of breast cancer. International Conference on Innovative Computing. IEEE; 2019.

Dalamaga M. Obesity, insulin resistance, adipocytokines and breast cancer: New biomarkers and attractive therapeutic targets. World J Exp Med. 2013; 3(3): 34-42. PMID: 24520544 DOI: 10.5493/wjem.v3.i3.34

DOI: http://dx.doi.org/10.30699/fhi.v10i1.255


  • There are currently no refbacks.