Fuzzy Logic in Breast Cancer Prediction: Unveiling Insights through Data Mining Techniques
Keywords:
Breast cancer prediction, data mining, k-fold cross-validationAbstract
The studies listed in this article use data mining techniques to find a trustworthy method of breast cancer prediction. The objective of this work is to develop a robust model that can predict the presence of breast cancer by comparing the clinical data of several patients. Four data mining techniques are used in this article: the AdaBoost tree, naïve bayes classifier, artificial neural network (ANN), and support vector machine (SVM). Furthermore, feature space is thoroughly studied in this work since it has such a large influence on the effectiveness and efficiency of the learning process. It is advised to combine PCA with other data mining algorithms that compress the feature space using a method similar to PCA. The purpose of this hybrid is to evaluate the impact of feature space reduction. Two often used test data sets are Wisconsin Breast Cancer Database (1991) and Wisconsin Diagnostic Breast Cancer (1995) and are used to evaluate the efficacy of these algorithms. Each model's test error is computed using the 10-fold cross-validation approach. The results of this study offer a comprehensive evaluation of the models as well as a detailed trade-off between various strategies. It is expected that in practical applications, feature identification results will contribute to patients' and physicians' prevention of breast cancer.
Downloads
References
Chaurasia V, Pal S, Tiwari BB. “Prediction of benign and malignant breast cancer using data mining techniques”. Journal of Algorithms & Computational Technology. 2018; 12(2):119-26.
Yue W, Wang Z, Chen H, Payne A, Liu X. “Machine learning with applications in breast cancer diagnosis and prognosis”. Designs. 2018; 2(2):13.
Banu B, Thirumalaikolundusubramanian P.” Comparison of Bayes Classifiers for Breast Cancer Classification”. Asian Pacific journal of cancer prevention ( APJCP). 2018; 19(10):2917-20.
Akinsola AF, Sokunbi MA, Onadokun IO. “Data Mining For Breast Cancer Classification”. International Journal of Engineering And Computer Science. 2017; 6(8): 22250-22258.
Mirajkar P, Lakshmi P. “Prediction of Cancer Risk in Perspective of Symptoms using Naïve Bayes Classifier”. International Journal of Engineering Research in Computer Science and Engineering. 2017; 4(9):145-149
Oskouei RJ, Kor NM, Maleki SA. “Data mining and medical world: breast cancers‟ diagnosis, treatment, prognosis and challenges”. American journal of cancer research. 2017; 7(3):610-27.
Forouzanfar, M. H., Foreman, K. J., Delossantos, A. M., Lozano, R., Lopez, A. D., Murray, C. J., and Naghavi, M., 2011, “Breast and Cervical Cancer in 187 Countries between 1980 and 2010: A Systematic Analysis,” The Lancet, 378(9801), 1461-1484.
Siegel, R., Ma J., Zou Z., and Jemal A., 2014, “Cancer Statistics 2014,” CA: A Cancer Journal for Clinicians, 64(1), 9-29.
Sotiriou, C., Neo, S. Y., McShane, L. M., Korn, E. L., Long, P. M., Jazaeri, A., and Liu, E. T., 2003, “Breast Cancer Classification and Prognosis Based on Gene Expression Profiles From A Population-based Study,” Proceedings of the National Academy of Sciences, 100(18), 10393-10398.
Sobin, L. H., Gospodarowicz, M. K., and Wittekind, C., TNM Classification of Malignant Tumors, John Wiley & Sons, 2011.
Rani, K. U., 2010, "Parallel Approach for Diagnosis of Breast Cancer using Neural Network Technique," International Journal of Computer Applications, 10(3), 1-5.
Delen, D., Walker, G. and Kadam, A., 2005, "Predicting Breast Cancer Survivability: A Comparison of Three Data Mining Methods," Artificial Intelligence in Medicine, 34(2), 113-127.
Choi, J. P., Han, T. H. and Park, R. W, 2009, "A Hybrid Bayesian Network Model for Predicting Breast Cancer Prognosis," Journal of Korean Society of Medical Informatics, 15(1), 49-57.
Bellaachia, A. and Guven E., 2006, "Predicting Breast Cancer Survivability Using Data Mining Techniques," Age, 58(13), 10-110.
Psychogios, G., Waldfahrer, F., Bozzato, A., and Iro, H., 2010, “Evaluation of the Revised TNM Classification in Advanced Laryngeal Cancer,” European Archives of Oto-Rhino-Laryngology, 267(1), 117-121.
Wang, W., Sun, X. W., Li, C. F., Lv, L., Li, Y. F., Chen, Y. B., and Zhou, Z. W., 2011, “Comparison of the 6th and 7th Editions of the UICC TNM Staging System for Gastric Cancer: Results of a Chinese Single institution Study of 1,503 Patients,” Annals of Surgical Oncology, 18(4), 1060-1067.
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
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.
