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A Review of Machine Learning Algorithms on Different Breast Cancer Datasets

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Big Data, Machine Learning, and Applications (BigDML 2021)

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 1053))

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Abstract

Machine Learning (ML) algorithms have been used widely in the domain of medical science especially in classifying clinical data. Random Forest, Decision Tree, K-Nearest Neighbor, Support Vector Machine, Naive Bayes, Logistic Regression, and Multilayer Perceptron are some of the ML algorithms used for classification and prediction of various diseases. This paper reviews 40 recent ML algorithms published for breast cancer classification and breast cancer prediction along with the associated data pre-processing and feature selection techniques. The paper identifies from literature the pre-processing, feature selection steps, and the ML algorithms used for classification and prediction of breast cancer and tabulates them according to the accuracy. The paper also briefs the aspects of three common clinical breast cancer datasets used to train most such ML algorithms. The review helps prospective researchers in identifying different aspects of research in the domain of providing ML solutions from breast cancer datasets using suitable pre-processing and feature selection techniques.

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Author information

Authors and Affiliations

  1. CITE, Manonmaniam Sundaranar University, Tirunelveli, India

    E. Jenifer Sweetlin & S. Saudia

Authors
  1. E. Jenifer Sweetlin
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  2. S. Saudia
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Corresponding author

Correspondence to E. Jenifer Sweetlin .

Editor information

Editors and Affiliations

  1. Computer Science & Engineering, National Institute of Technology Silchar, Silchar, Assam, India

    Malaya Dutta Borah

  2. Computer Science & Engineering, National Institute of Technology Silchar, Silchar, Assam, India

    Dolendro Singh Laiphrakpam

  3. Computer Science & Engineering, Indian Institute of Technology Ropar, Rupnagar, Punjab, India

    Nitin Auluck

  4. Department of Automation and Applied Informatics, Aurel Vlaicu University of Arad, Arad, Romania

    Valentina Emilia Balas

Annexure-Expansion of Acronyms Mentioned in Sect. 3

Annexure-Expansion of Acronyms Mentioned in Sect. 3

AB-Ada Boost

LR-Logistic Regression

ANN-Artificial Neural Networks

LSVM-Library for SVM

AR-Association Rules

LV-Low Variance

BC-Breast Cancer

MC-Multiclass Classifier

BF-Best First

MLP-Multilayer Perceptron

BLR-Bayesian Logistic Regression

MMN-Min–Max Normalization

BN-Bayes Net

MO-Model Overfitting

BPN-Back Propagation Network

MV-Missing Values

BS-Backward Selection

NB-Naive Bayes

B-SVM-Binary SVM

NCA-Neighborhood Component Analysis

CART-Classification and Regression Tree

NN-Neural Network

CFS-Correlation-based Feature Selection

PCA-Principal Component Analysis

CSE-Consistency-based Subset Evaluation

PCC-Pearson Correlation Coefficient

DF-Discretization Filters

PNN-Probabilistic Neural Network

DT-Decision Tree

PO-Parameter Optimization

DTa-Decision Table

PSO-Particle Swarm Optimization

ELM-Extreme Learning Machine

PURELIN-Linear Transfer function

FE-Feature Extraction

QK-Quadratic Kernel

FDT-Fuzzy Decision Tree

RBF-Radial Basis Function

F-RISM-Fuzzy Rough Instance selection method

RDF-Random Decision Forest

F-RNN-Fuzzy Rough Nearest Neighbor

RDT-Random Decision Tree

FS-Feature Selection

RF-Random Forest

GA-Genetic Algorithm

RFE-Recursive Feature Elimination

GB-Gradient Boosting

RRA-Re-Ranking Algorithm

GNB-Gaussian Naive Bayes

RS-Random Sampling

GP-Genetic Programming

RT-Random Tree

GR-Gain Ratio

RUS-Random Under Sampling

GRS-Greedy Stepwise

SE-Standard Error

GS-Genetic Search

SEv-Subset Evaluation

HV-Hard Voting

SGD-Stochastic Gradient Descent

ID3-Iterative Dichotomiser 3

SMO-Sequential Minimal Optimization

IA-Irrelevant Attribute

SMOTE-Synthetic Minority Over-sampling Technique

ICA-Independent Component Analysis

SS-Standard Scaler

IG-Information Gain

SV-Soft Voting

IGAE-Information Gain Attribute Evaluation

SVM-Support Vector Machine

J-Rip-J-Repeated Incremental Pruning

TANSIG-Hyperbolic Tangent Sigmoid

KNN-K-Nearest Neighbor

TF-Transfer Function

K-SVM-Kernel SVM

UFS-Univariate Feature Selection

LASSO-Least Absolute Shrinkage and Selection Operator

VP-Voted Perceptron

Lazy-IBK-Instance-Based K-NN

WBFS-Wrapper-Based Feature Selection

Lazy K-star-KNN Star

WKNN-Weighted KNN

LDA-Linear Discriminant Analysis

WNB-Weighted Naive Bayes

LOGSIG-Log Sigmoid Activation Function

WSE-Wrapper Subset Evaluation

XGB-Extreme Gradient Boosting

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Sweetlin, E.J., Saudia, S. (2024). A Review of Machine Learning Algorithms on Different Breast Cancer Datasets. In: Borah, M.D., Laiphrakpam, D.S., Auluck, N., Balas, V.E. (eds) Big Data, Machine Learning, and Applications. BigDML 2021. Lecture Notes in Electrical Engineering, vol 1053. Springer, Singapore. https://doi.org/10.1007/978-981-99-3481-2_51

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  • DOI: https://doi.org/10.1007/978-981-99-3481-2_51

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  • Print ISBN: 978-981-99-3480-5

  • Online ISBN: 978-981-99-3481-2

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