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20 June 2024
Reseach Article

Applying Machine Learning Algorithms for Early Prediction of Breast Cancer

by B. Srinivas, M. Sriram, V. Ganesan
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 186 - Number 23
Year of Publication: 2024
Authors: B. Srinivas, M. Sriram, V. Ganesan
10.5120/ijca2024923683

B. Srinivas, M. Sriram, V. Ganesan . Applying Machine Learning Algorithms for Early Prediction of Breast Cancer. International Journal of Computer Applications. 186, 23 ( May 2024), 54-63. DOI=10.5120/ijca2024923683

@article{ 10.5120/ijca2024923683,
author = { B. Srinivas, M. Sriram, V. Ganesan },
title = { Applying Machine Learning Algorithms for Early Prediction of Breast Cancer },
journal = { International Journal of Computer Applications },
issue_date = { May 2024 },
volume = { 186 },
number = { 23 },
month = { May },
year = { 2024 },
issn = { 0975-8887 },
pages = { 54-63 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume186/number23/applying-machine-learning-algorithms-for-early-prediction-of-breast-cancer/ },
doi = { 10.5120/ijca2024923683 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-05-31T22:32:03.082038+05:30
%A B. Srinivas
%A M. Sriram
%A V. Ganesan
%T Applying Machine Learning Algorithms for Early Prediction of Breast Cancer
%J International Journal of Computer Applications
%@ 0975-8887
%V 186
%N 23
%P 54-63
%D 2024
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Breast cancer is a devastating illness impacting millions of women globally. Early prediction is vital for successful treatment and improved survival rates. Machine learning algorithms have come out as one of the most efficient tools for classifying and diagnosing breast cancer, presenting promising solutions for early prediction and enhanced patient outcomes. The study utilised various machine learning classifiers to categorise breast cancer data: MLP (Multi-layer Perceptron classifier), Support Vector Machines (SVMs), Random Forests (RFs), Logistic Regression (LR), K-Nearest Neighbor (KNN), and Decision Trees (DTs) Classifier. Each classifier underwent training and evaluation using the Wisconsin Breast Cancer Dataset, a widely utilised benchmark dataset with 569 instances featuring characteristics extracted from fine needle aspirates of breast mass lesions. The effectiveness of each classifier was assessed employing various metrics, including accuracy, F1-score, specificity, and sensitivity. Experimental results revealed that MLP (Multi-layer Perceptron) displayed superior performance among the tested classifiers, achieving an accuracy of 95.07%, an F1-score of 93.33%, a specificity of 94.44%, and a sensitivity of 99.22%. SVM Classifier closely followed, attaining accuracies of 94.37% and 97.35%, respectively. The findings highlight the potential of machine learning algorithms, especially Multi-Layer Perceptron, which can accurately classify breast cancer datasets and predict breast cancer. The high accuracy and sensitivity achieved by Multi-Layer Perceptron suggest its suitability for early cancer prediction, enabling prompt intervention and improved treatment outcomes.

References
  1. Bowman, M., Debray, S. K., and Peterson, L. L. 1993. Reasoning about naming systems. .
  2. Ding, W. and Marchionini, G. 1997 A Study on Video Browsing Strategies. Technical Report. University of Maryland at College Park.
  3. Fröhlich, B. and Plate, J. 2000. The cubic mouse: a new device for three-dimensional input. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
  4. Tavel, P. 2007 Modeling and Simulation Design. AK Peters Ltd.
  5. Sannella, M. J. 1994 Constraint Satisfaction and Debugging for Interactive User Interfaces. Doctoral Thesis. UMI Order Number: UMI Order No. GAX95-09398., University of Washington.
  6. Forman, G. 2003. An extensive empirical study of feature selection metrics for text classification. J. Mach. Learn. Res. 3 (Mar. 2003), 1289-1305.
  7. Brown, L. D., Hua, H., and Gao, C. 2003. A widget framework for augmented interaction in SCAPE.
  8. Y.T. Yu, M.F. Lau, "A comparison of MC/DC, MUMCUT and several other coverage criteria for logical decisions", Journal of Systems and Software, 2005, in press.
  9. Spector, A. Z. 1989. Achieving application requirements. In Distributed Systems, S. Mullender
Index Terms

Computer Science
Information Sciences

Keywords

Machine learning breast cancer diagnosis early Prediction treatment survival rates.