BMC Medical Informatics and Decision Making

Table 3 Evaluation of classification algorithms

From: Prediction of lymphedema occurrence in patients with breast cancer using the optimized combination of ensemble learning algorithm and feature selection

Classification
Evaluation	Confusion Matrix		Algorithm
FPR = 0. 2500	143	547	SVM (Linear)
FRR = 0. 2072	143	547
Accuracy = 0. 7706	555	185
Cost = 0. 2291	555	185
FPR = 0. 1486	53	637	SVM (RBF)
FRR = 0. 0768	53	637
Accuracy = 0. 8860	630	110
Cost = 0. 1135	630	110
FPR = 0. 2365	22	668	SVM (Polynomial)
FRR = 0. 3190	22	668
Accuracy = 0. 8622	565	175
Cost = 0. 1363	565	175
FPR = 0. 2757	142	548	LDA
FRR = 0. 2058	142	548
Accuracy = 0. 7580	536	204
Cost = 0. 2415	536	204
FPR = 0. 3959	71	619	KNN
FRR = 0. 1029	71	619
Accuracy = 0. 7455	447	293
Cost = 0. 2525	447	293
FPR = 0. 4514	120	570	Bayes
FRR = 0. 1739	120	570
Accuracy = 0. 6825	406	334
Cost = 0. 3155	406	334
FPR = 0. 1622	138	552	C5
FRR = 0. 2000	138	552
Accuracy = 0. 8196	620	120
Cost = 0. 1807	620	120
FPR = 0. 2216	189	501	MLP
FRR = 0. 2739	189	501
Accuracy = 0. 7531	576	164
Cost = 0. 2472	576	164

Back to article page

ISSN: 1472-6947

Contact us

General enquiries: journalsubmissions@springernature.com