Table 4.

Performance of various machine learning algorithms using blood profile data for the classification of Breast cancer metastasis.

Classification models	Before removal of outliers					After removal of outliers
	Accuracy	Recall	Precision	F1 Score	AUC	Accuracy	Recall	Precision	F1 Score	AUC
Logistic regression	0.73	0.98	0.73	0.84	0.74	0.61	0.96	0.59	0.73	0.68
KNN	0.7	0.87	0.75	0.81	0.73	0.78	0.78	0.81	0.79	0.79
Decision trees	0.64	0.75	0.75	0.75	0.68	0.83	0.86	0.83	0.85	0.87
Random forest	0.73	0.98	0.74	0.84	0.74	0.83	0.89	0.81	0.85	0.85
SVM linear	0.72	1	0.72	0.84	0.74	0.55	1	0.55	0.71	0.62
Polynomial SVM	0.73	0.99	0.73	0.84	0.74	0.82	0.86	0.82	0.84	0.85
Radial SVM	0.72	1	0.72	0.84	0.74	0.55	1	0.55	0.71	0.62
Gradient boosting	0.72	0.99	0.72	0.84	0.72	0.81	0.95	0.76	0.84	0.85
XGBOOST	0.73	0.99	0.73	0.84	0.7	0.78	0.93	0.74	0.83	0.85

The table represents the comparative performance of ML models before and after the removal of the outliers.