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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
Year of Publication: 2021
Taskin Noor Turna, Mst. Alema Khatun

Taskin Noor Turna and 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):44-48, September 2021. BibTeX

	author = {Taskin Noor Turna and Mst. Alema Khatun},
	title = {Performance Analysis of Different Classifiers used in Detecting Benign and Malignant Cells of Breast Cancer},
	journal = {International Journal of Computer Applications},
	issue_date = {September 2021},
	volume = {183},
	number = {27},
	month = {Sep},
	year = {2021},
	issn = {0975-8887},
	pages = {44-48},
	numpages = {5},
	url = {},
	doi = {10.5120/ijca2021921661},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}


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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SVM, XGBoost, performance, classification, breast cancer