Predicting Breast Cancer Leveraging Supervised Machine Learning Techniques

Table 6

Comparison of machine learning algorithms on Wisconsin Breast Cancer dataset.

Author	Year	Dataset	Imbalance handling	Feature selection	Features	Classifier	Validation type	Accuracy achieved (%)
Aryal & Paudel [37]	2020	WDBC	—	—	30	Gradient; Boosting	10-fold	98.88%
Ahmet Saygili [38]	2018	WDBC	—	Gain Ratio	24	Random Forest	10-fold	98.77%
Dubey et al. [39]	2016	WDBC	—	—	—	-means clustering	—	92.00%
Salama et al. [41]	2016	WDBC	—	—	30	SMO	10-fold	97.71%
Our approach	2020	WDBC	Normalization by standardization	Correlation-based selection & RFE	11	MLP	5-fold	99.12%