Early Detection of Breast Cancer in Women Using a Cost-effective Procedure

Document Type : Original Article

Author

Department of Computer Science, University of Sistan and Baluchestan ,Zahedan, Iran

Abstract

Breast cancer is considered to be the second most common type of cancer affecting the female population worldwide. It is estimated that more than 508 000 women died in 2011 as a result of breast cancer. The survival rates of breast cancer are lower in less developed countries mainly due to the absence of early detection methods resulting in a great percentage of women showing with late-stage disease. Early detection and medical diagnosis are known to be the most effective solution to minimize the risk of tumor development and progression.  There are different methods for Early detection of breast cancer which include screening tests and clinical breast exams performed by a well-trained health professional. Due to a lack of facilities and cost, many women in less developed countries may not be able to use the mentioned methods. The objective associated with this research was to achieve an affordable and cost-effective prediction model of breast cancer based on anthropometric data and parameters that can easily be collected in a routine and regular blood test.  For every one of the 166 individuals number of clinical features such as age, Body Mass Index (MBI), serum glucose levels, plasma levels of insulin, etc. were measured and observed. Various learning algorithms including Support Vector Machines (SVM), K-Nearest Neighbors (K-NN) and logistic regression(LR), etc. have been applied and compared with one another.   The result shows that SVM and K-NN models perform well and allow prediction of breast cancer in women with accuracy more than 78%, the sensitivity of 78% and 79%, and Specificity value is 77% and 79% respectively.   

Keywords


Assiri, A. M., & Kamel, H. F. (2015). (2015). Evaluation of diagnostic and predictive value of serum adipokines: Leptin, resistin and visfatin in postmenopausal breast cancer. Obes Res Clin Pract., 10(4):442-53.
Bartosik, M., & Jirakova, L. (2019). Electrochemical analysis of nucleic acids as potential cancer biomarkers. Current Opinion in Electrochemistry, 14, 96–103.
Coleman, M., Quaresma, M., Berrino, F., Lutz, J.M., Angelis, R., Capocaccia, R., & et al. (2008). (2008). Cancer survival in five continents: a worldwide population-based study (CONCORD) Lancet Oncol.   9:730–756. 
Chaurasia, V., & Pal, S. (2014). (2014).  Data mining techniques: to predict and resolve breast cancer survivability. Int J Comput Sci Mobile Comput., 3: 10–22.
Crisóstomo, J., & et al. (2016). (2016). Hyperresistinemia and metabolic dysregu- lation: the close crosstalk in obese breast cancer. Endocrine, 53(2):433-42.
Guan, Z., Yu, H., Cuk, K., Zhang, Y., & Brenner, H. (2019).. (2019). Whole-Blood DNA methylation markers in early detection of breast cancer: A systematic literature review. Cancer Epidemiol Biomarkers Prev. 28, 496–505.
“Mathworks.com.” Supervised Learning Workflow and Algorithms - MATLAB & Simulink - MathWorks , it.mathworks .com/  help/  stats/supervised-learning-machine-learning-workflow-and-algorithms.html.
Patrício, M., Pereira, J., Crisóstomo, J., Matafome, P., Gomes, M., Seiça, R., & Caramelo, F. (2018). Using Resistin, glucose, age and BMI to predict the presence of breast cancer. BMC Cancer. 18(1).
,. &(2013).
Toprak, A. (2018). Extreme Learning Machine (ELM)-Based Classification of Benign and Malignant Cells in Breast Cancer. Medical Science Monitor, 24, 6537–6543.
Wang, L. (2017). (2017). Early diagnosis of breast cancer. Sensors. doi: 10.3390/s17071572.  17(7): 1572.
Volume 2, Issue 1
January 2021
Pages 1-6
  • Receive Date: 20 March 2020
  • Revise Date: 04 April 2020
  • Accept Date: 18 April 2020
  • First Publish Date: 01 January 2021