Machine learning models applied in analyzing breast cancer classification accuracy

Bokhare A.; Jha P.

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Machine learning models applied in analyzing breast cancer classification accuracy

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There have been many attempts made to classify breast cancer data, since this classification is critical in a wide variety of applications related to the detection of anomalies, failures, and risks. In this study machine learning (ML) models are reviewed and compared. This paper presents the classification of breast cancer data using various ML models. The effectiveness of models comparatively evaluated through result using benchmark of accuracy which was not done earlier. The models considered for the study are k-nearest neighbor (kNN), decision tree classifier, support vector machine (SVM), random forest (RF), SVM kernels, logistic regression, Naïve Bayes. These classifiers were tested, analyzed and compared with each other. The classifier, decision tree, gets the highest accuracy i.e. 97.08 among all these models is termed as the best ML algorithm for the breast cancer data set. © 2023, Institute of Advanced Engineering and Science. All rights reserved.

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《IAES International Journal of Artificial Intelligence》 |2023年第3期|1370-1377|共8页
作者
Bokhare A.; Jha P.;
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作者单位

Symbiosis Institute of Computer Studies and Research (SICSR), A Constituent of Symbiosis International (Deemed University), Atur Centre;

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原文格式 PDF
正文语种英语
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Breast cancer; Decision tree classifier; k-nearest neighbor; Logistic regression; Naïve Bayes; Random forest; Support vector machine;

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Machine learning models applied in analyzing breast cancer classification accuracy

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