Performance Analysis of ANN and Naive Bayes Classification Algorithm for Data Classification
Keywords:ANN, Breast Cancer, Classification, Artificial Neural Network, Machine Learning Database, Naïve Bayes
Classification is an important data mining technique with a wide range of applications to classify the various types of data existing in almost all areas of our lives. The purpose of this discovery study can be used to estimate the potential of having breast cancer by taking advantage of anthropometric data and collected routine blood analysis parameters. The study was performed using data from patients who were admitted to the clinic with the suspicion of breast cancer. The values of Age (years), BMI (kg/m2), Glucose (mg/dL), Insulin (µU/mL), HOMA, Leptin (ng/mL), Adiponectin (µg/mL), Resistin (ng/mL), MCP-1(pg/dL) were used. In our study, classification algorithms were applied to the data and they were asked to estimate the disease diagnosis. The classification performance of Artificial neural networks and Naïve Bayes classifiers which were applied to data with 9 inputs and one output were calculated and theperformance results were compared. This article sheds light on the performance evaluation based on correct and incorrect data classification examples using ANN and Naïve Bayes classification algorithm. When we look at the performances obtained, it is predicted that using the anthropometric data and the collected routine blood analysis parameters, the potential for diagnosing breast cancer is high using these data.
URL1, https://www.mayoclinic.org/diseases-conditions/breast -cancer/symptoms-causes/syc-20352470. Last Access(12.12.2018).
Nyante, S.J., et al., The association between mammographic calcifications and breast cancer prognostic factors in a population‐based registry cohort. Cancer, 2017. 123(2): p. 219-227.
Crisóstomo, J., et al., Hyperresistinemia and metabolic dysregulation: a risky crosstalk in obese breast cancer. Endocrine, 2016. 53(2): p. 433-442.
Saritas, I., Prediction of breast cancer using artificial neural networks. Journal of Medical Systems, 2012. 36(5): p. 2901-2907.
URL2, http://archive.ics.uci.edu/ml/datasets/Breast+Cancer+Coimbra#. Last Access (05.12.2018).
Bebis, G. and M. Georgiopoulos, Feed-forward neural networks. IEEE Potentials, 1994. 13(4): p. 27-31.
Shu, X., et al., Associations of obesity and circulating insulin and glucose with breast cancer risk: a Mendelian randomization analysis. International journal of epidemiology, 2018.
Anderson, K.N., R.B. Schwab, and M.E. Martinez, Reproductive risk factors and breast cancer subtypes: a review of the literature. Breast cancer research and treatment, 2014. 144(1): p. 1-10.
Guerrieri-Gonzaga, A., et al., Abstract P4-11-16: Low serum adiponectin level is an independent risk factor of DCIS in postmenopausal women at increased risk of breast cancer. 2015, AACR.
Hopfield, J.J., Artificial neural networks. IEEE Circuits and Devices Magazine, 1988. 4(5): p. 3-10.
Bhardwaj, A. and A. Tiwari, Breast cancer diagnosis using genetically optimized neural network model. Expert Systems with Applications, 2015. 42(10): p. 4611-4620.
Haykin, S.S., et al., Neural networks and learning machines. Vol. 3. 2009: Pearson Upper Saddle River.
Rumelhart, D.E., G.E. Hinton, and R.J. Williams, Learning representations by back-propagating errors. nature, 1986. 323(6088): p. 533.
Dimitoglou, G., Adams, J. A., & Jim, C. M. (2012). Comparison of the C4. 5 and a Naïve Bayes classifier for the prediction of lung cancer survivability. arXiv preprint arXiv:1206.1121.
Patil, T. R., & Sherekar, S. S. (2013). Performance analysis of Naive Bayes and J48 classification algorithm for data classification. International journal of computer science and applications, 6(2), 256-261.
How to Cite
All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.
IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.