Table 1.

Breast cancer detection research using different machine learning algorithms.

Paper title	Datasets	Algorithms	Results
Integration of data mining classification techniques and ensemble learning for predicting the type of breast cancer recurrence [3], 2019	Breast Cancer	NB, SVM, GRNN and J48	GRNN & J48 accuracy: 91% NB & SVM: 89%
A study on prediction of breast cancer recurrence using data mining techniques [4], 2017	WPBC	Classification: KNN, SVM, NB and C5.0, Clustering: K-means, EM, PAM and Fuzzy c-means	Classification accuracy is better than clustering, SVM & C5.0: 81%
Predicting breast cancer recurrence using effective classification and feature selection technique [5], 2016	WPBM	NB, C4.5, SVM	NB: 67.17%, C4.5: 73.73%, SVM: 75.75%
Using machine learning algorithms for breast cancer risk prediction and diagnosis [6], 2016	WBC	SVM, C4.5, NB, KNN	SVM outperform others: 97.13%
Study and analysis of breast cancer cell detection using Naïve Bayes, SVM and ensemble algorithms [7], 2016	WDBC	NB, SVM, Ensemble	SVM: 98.5%, NB & Ensemble: 97.3%
Analysis of Wisconsin breast cancer dataset and machine learning for breast cancer detection [8], 2015	WDBC	NB, J48	NB: 97.51%, J48: 96.5%
Comparative study on different classification techniques for breast cancer dataset [9], 2014	Breast Cancer	J48, MLP, rough set	J48: 79.97%, MLP: 75.35%, rough set: 71.36%
A novel approach for breast cancer detection using data mining techniques [10], 2014	WBC	SMO, IBK, BF Tree	SMO: 96.19%, IBK: 95.90%, BF Tree: 95.46%
Experiment comparison of classification for breast cancer diagnosis [11], 2012	WBC WDBC WPBC	J48, SMO, MLP, NB, IBK	In WBC: MLP & J48: 97.2818%. In WDBC: SMO: 97.7% or fusion on SMO & MLP: 97.7% In WPBC: fusion of MLP, J48, SMO and IBK: 77%
Analysis of feature selection with classification: breast cancer datasets [12], 2011	WBC WDBC Breast Cancer	Decision Tree with and without feature selection	Feature selection enhances the results WBC: 96.99% WDBC: 94.77% Breast Cancer: 71.32%