scholarly journals An Efficient Hybrid Genetic-Grey Wolf Based Neural Network (G2NN) for Breast Cancer Data Classification

2020 ◽  
Vol 10 (1) ◽  
pp. 268-271
Keyword(s):  
Breast Cancer ◽  
Neural Network ◽  
Machine Learning ◽  
Genetic Algorithm ◽  
Support Vector Machine ◽  
Data Classification ◽  
Support Vector ◽  
Grey Wolf ◽  
Breast Cancer Data ◽  
Cancer Data

Machine learning is the one of the famous Artificial Intelligence (AI) technique. Data Mining or Machine Learning techniques are most popular in medical diagnosis, classification, forecasting etc. K-Nearest Neighbor, SVM (Support Vector Machine), DT (Decision Tree),RF (Random Forest),NN (Neural Network) are famous classification algorithms. Neural Network is one of the popular techniques, which is used to refine the verdict of breast cancer. A neural network is otherwise known as Artificial Neural Network(ANN), which is mimicking of biological neurons of human brain. Genetic Algorithm (GA) is emerged bio inspired technique. Selection, Crossover, and Mutation are three operations in Genetic Algorithm. The performance of a genetic algorithm depends on the genetic operators, particularly crossover operator. Grey Wolfoptimization algorithm is inspired from hunting of wolf strategy. Alphas, Beta, Gamma are the three levels ofprocesses. In this paper, a novel hybrid Genetic Grey Wolf based Neural Network is introduced and we named it as G2NN. In the field of medical, we need more accuracy when compared to other field, because it relates to human life. Many researchers found new novel ideas for breast cancer data classification using neural network model. Among many diseases,Breast Cancer is one of the unsafe diseases among women in Indiaand in addition to the whole world. The early detection of cancer helps in curing the disease completely. In many research areas Genetic Algorithm and Grey wolf algorithm are used to train neurons in order to yield good accuracy. In this manuscript, a new GeneticGrey Wolf optimizer based Neural Network is introduced and we compare the proposed work with other techniques like SVM(Support Vector Machine),NN (Neural Network), Genetic based Neural Network, Grey wolf based Neural Network and the experimental results of proposed work produced better result. The proposed algorithm produces 98.9 % of accuracy on UCI Wisconsin breast cancer dataset.

Detection of Breast Cancer Using Machine Learning Support Vector Machine Algorithm

2019 ◽  
Vol 16 (2) ◽  
pp. 441-444
Author(s):  
D. V. Soundari ◽  
R. Padmapriya ◽  
C. Thirumariselvi ◽  
N. Nanthini ◽  
K. Priyadharsini
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Support Vector Machine ◽  
Support Vector ◽  
Learning Support ◽  
Support Vector Machine Algorithm ◽  
Breast Cancer Data ◽  
Data Set ◽  
Cancer Data ◽  
Hormone Imbalance

A woman majorly suffers due to breast cancer which is due to hormone imbalance. It leads to huge death in recent years. Early detection of the breast cancer is more important to prevent human lives. Image Processing plays an important to classify and detect the same. So this paper proposes machine learning based cancer classification using support vector machine with Wisconsin breast cancer data set.

Breast Cancer Diagnosis Using an Ensemble Transfer Support Vector Machine

2021 ◽  
Vol 11 (2) ◽  
pp. 332-336
Author(s):  
Lifang Peng ◽  
Kefu Chen ◽  
Bin Huang ◽  
Leyuan Zhou
Keyword(s):  
Breast Cancer ◽  
Support Vector Machine ◽  
Support Vector ◽  
Breast Cancer Data ◽  
Generalization Performance ◽  
Source Domain ◽  
Cancer Data ◽  
Adaboost Algorithm ◽  
Svm Algorithm ◽  
Ensemble Strategy

As the number of breast cancer patients increases and the age of onset is younger, early detection and prevention have become the key to prevention and treatment of breast cancer. At present, many classification or clustering algorithms are used to diagnose breast cancer data. However, these algorithms directly lose the minimum source domain information, resulting in a significant improvement in the recognition rate. Based on this, this paper proposes an ensemble transfer support vector machine (ET-SVM) algorithm based on classic support vector machine (SVM). The algorithm can effectively use the knowledge in the source domain to guide the learning of the target task. The result of a single SVM is usually the local optimal solution. And its performance is unstable and its generalization performance is poor. Therefore, this article introduces an ensemble strategy based on AdaBoost algorithm. Experiments on the Wisconsin breast cancer data set proved that the proposed ET-SVM algorithm can achieve better classification results and good generalization performance.

scholarly journals Hyperparameter Tuning and Pipeline Optimization via Grid Search Method and Tree-Based AutoML in Breast Cancer Prediction

2021 ◽  
Vol 11 (10) ◽  
pp. 978
Author(s):  
Siti Fairuz Mat Radzi ◽  
Muhammad Khalis Abdul Karim ◽  
M Iqbal Saripan ◽  
Mohd Amiruddin Abdul Rahman ◽  
Iza Nurzawani Che Isa ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Model Selection ◽  
Principal Component ◽  
Receiver Operating Curve ◽  
Support Vector ◽  
Grid Search ◽  
Breast Cancer Data ◽  
Data Set ◽  
Cancer Data

Automated machine learning (AutoML) has been recognized as a powerful tool to build a system that automates the design and optimizes the model selection machine learning (ML) pipelines. In this study, we present a tree-based pipeline optimization tool (TPOT) as a method for determining ML models with significant performance and less complex breast cancer diagnostic pipelines. Some features of pre-processors and ML models are defined as expression trees and optimal gene programming (GP) pipelines, a stochastic search system. Features of radiomics have been presented as a guide for the ML pipeline selection from the breast cancer data set based on TPOT. Breast cancer data were used in a comparative analysis of the TPOT-generated ML pipelines with the selected ML classifiers, optimized by a grid search approach. The principal component analysis (PCA) random forest (RF) classification was proven to be the most reliable pipeline with the lowest complexity. The TPOT model selection technique exceeded the performance of grid search (GS) optimization. The RF classifier showed an outstanding outcome amongst the models in combination with only two pre-processors, with a precision of 0.83. The grid search optimized for support vector machine (SVM) classifiers generated a difference of 12% in comparison, while the other two classifiers, naïve Bayes (NB) and artificial neural network—multilayer perceptron (ANN-MLP), generated a difference of almost 39%. The method’s performance was based on sensitivity, specificity, accuracy, precision, and receiver operating curve (ROC) analysis.

Breast cancer Data classification Using Machine Learning Mechanisms

2019 ◽  
Vol 10 (5) ◽  
pp. 214
Author(s):  
R Shiva Shankar ◽  
V Mnssvkr Gupta ◽  
K V S S Murthy ◽  
Chinta Someswara Rao
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Data Classification ◽  
Breast Cancer Data ◽  
Learning Mechanisms ◽  
Cancer Data
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