Breast Cancer Detection in the IOT Health Environment Using Modified Recursive Feature Selection

Table 9

Proposed study classification performance and results of other previously proposed methods.

Reference	Method	Accuracy (%)
[38]	PCA-AE-Ada	85
[39]	ACO-SVM	95.98
[35]	GA-SVM	97.19
[35]	PSO-SVM	97.37
[26]	GA-MOO-NN	98.85
[14]	PCA-SVM	96.84
[40]	Breast cancer diagnosis techniques using SVM, PNN, and MLP	97.80
[11]	Classification system using fuzzy-GA method	97.36
[20]	Classification system using mixture ensemble of convolutional neural network	96.39
[41]	SAE-SVM	98.25
[42]	Prediction of breast cancer using SVM and K-NN	98.57
[43]	Breast cancer diagnosis using adaptive voting ensemble machine learning algorithm	98.50
[44]	Cost sensitivity SVM with IG for FS and breast cancer diagnosis	98.83
Proposed method	REF-SVM	99