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Performance Analysis of Different Classifiers used in Detecting Benign and Malignant Cells of Breast Cancer
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 183 - Number 27 |
| Year of Publication: 2021 |
| Authors: Taskin Noor Turna, Mst. Alema Khatun |
10.5120/ijca2021921661
|
Taskin Noor Turna, Mst. Alema Khatun . Performance Analysis of Different Classifiers used in Detecting Benign and Malignant Cells of Breast Cancer. International Journal of Computer Applications. 183, 27 ( Sep 2021), 44-48. DOI=10.5120/ijca2021921661
Abstract
Breast cancer is the most common disease now a days. To get an early detection the target is to find an efficient way to use scientific investigation, because early detection is the only way to remove cancer cell. To predict the accuracy of breast cancer detection, researchers have used different classification techniques. In this paper random forest, Support vector machine, XGBoost, Decision Tree, Naïve Bayes and AdaBoost have been used to analyze and compare the performance. A comparative study is done on these five classifiers using different accuracy measurements like performance, accuracy rate. This study shows that XGBoost gives the high performance among others.
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