Main Article Content

Abstract

There are considerably more breast cancer fatalities each year. The most common kind of cancer and the main cause of death in women worldwide is this one. A healthy life depends on every development in the prognosis and diagnosis of cancer sickness. The standard of treatment and patient survival rate must be updated, thus an accurate cancer prognosis is crucial. Research has demonstrated that machine learning approaches are effective for the early detection and prediction of breast cancer and have grown in popularity. Random Forest, Logistic Regression, Xtreme Gradient, and AdaBoost Classifier are trained on the Breast Cancer Wisconsin Diagnostic dataset, and their efficacy is assessed and compared in this study using ensemble classifier and machine learning. The major objective of this study is to identify the most effective ensemble and machine learning classifiers for breast cancer detection and diagnosis in terms of Accuracy and AUC Score.

Keywords

Prediction Breast cancer Machine Learning Ensemble Classifier

Article Details

How to Cite
Arshad, M. W. (2023). PREDICTION AND DIAGNOSIS OF BREAST CANCER USING MACHINE LEARNING AND ENSEMBLE CLASSIFIERS. CENTRAL ASIAN JOURNAL OF MATHEMATICAL THEORY AND COMPUTER SCIENCES, 4(1), 49-56. https://doi.org/10.17605/OSF.IO/9CFN6

References

  1. 1. ‘WHO | Breast cancer’, WHO. http://www.who.int/cancer/prevention/diagnosis-screening/breast-cancer/en/ (accessed Feb. 18, 2020).
  2. 2. Datafloq - Top 10 Data Mining Algorithms, Demystified. https://datafloq.com/read/top-10-data-mining-algorithmsdemystified/1144. Accessed December 29, 2015.
  3. 3. "Breast Cancer - Diagnosis And Treatment - Mayo Clinic". Mayoclinic.Org, 2022, https://www.mayoclinic.org/diseases-conditions/breast-cancer/diagnosis-treatment/drc-20352475. Accessed 23 July 2022.
  4. 4. S. Nayak and D. Gope, "Comparison of supervised learning algorithms for RF-based breast cancer detection," 2017 Computing and Electromagnetics International Workshop (CEM), Barcelona, 2017, pp.
  5. 5. B.M. Gayathri and C. P. Sumathi, "Comparative study of relevance vector machine with various machine learning techniques used for detecting breast cancer," 2016 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC), Chennai, 2016, pp. 1-5.
  6. 6. H. Asri, H. Mousannif, H. A. Moatassime, and T. Noel, ‘Using Machine Learning Algorithms for Breast Cancer Risk Prediction and Diagnosis’, Procedia Computer Science, vol. 83, pp. 1064–1069, 2016, doi: 10.1016/j.procs.2016.04.224
  7. 7. Dhar V. Data science and prediction. Commun. ACM. 2013;56:64–73. doi: 10.1145/2500499.
  8. 8. Aruna S., Rajagopalan S., Nandakishore L. Knowledge based analysis of various statistical tools in detecting breast cancer. Comput. Sci. Inf. Technol. 2011;2:37–45.
  9. 9. Chaurasia V., Pal S. Data mining techniques: To predict and resolve breast cancer survivability. Int. J. Comput. Sci. Mob. Comput. 2014;3:10–22.
  10. 10. Asri H., Mousannif H., Al Moatassime H., Noel T. Using machine learning algorithms for breast cancer risk prediction and diagnosis. Procedia Comput. Sci. 2016;83:1064–1069. doi: 10.1016/j.procs.2016.04.224.
  11. 11. Delen D., Walker G., Kadam A. Predicting breast cancer survivability: A comparison of three data mining methods. Artif. Intell. Med. 2005;34:113–127. doi: 10.1016/j.artmed.2004.07.002.
  12. 12. Bernal J.L., Cummins S., Gasparrini A. Interrupted time series regression for the evaluation of public health interventions: A tutorial. Int. J. Epidemiol. 2017;46:348–355.
  13. 13. Wang H., Yoon W.S. Breast cancer prediction using data mining method; Proceedings of the 2015 Industrial and Systems Engineering Research Conference; Nashville, TN, USA. 30 May–2 June 2015.
  14. 14. Williams T.G.S., Cubiella J., Griffin S.J. Risk prediction models for colorectal cancer in people with symptoms: A systematic review. BMC Gastroenterol. 2016;16:63. doi: 10.1186/s12876-016-0475-7.
  15. 15. Nithya R., Santhi B. Classification of normal and abnormal patterns in digital mammograms for diagnosis of breast cancer. Int. J. Comput. Appl. 2011;28:0975–8887. doi: 10.5120/3391-4707.
  16. 16. Hastie T, Tibshirani R, Friedman J. The elements of statistical learning. New York, NY: Springer-Verlag;2001.
  17. 17. Chengsheng, Tu & Huacheng, Liu & Bing, Xu. (2017). AdaBoost typical Algorithm and its application research. MATEC Web of Conferences. 139. 00222. 10.1051/matecconf/201713900222.
  18. 18. "Understanding AUC - ROC Curve". Medium, 2021, https://towardsdatascience.com/understanding-auc-roc-curve-68b2303cc9c5. Accessed 23 July 2022.