Microarray breast cancer data classification using machine learning methods

2018 ◽  
Author(s):  
Siyabend Turgut ◽  
Mustafa Dagtekin ◽  
Tolga Ensari
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Data Classification ◽  
Breast Cancer Data ◽  
Learning Methods ◽  
Cancer Data ◽  
Machine Learning Methods

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.

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

scholarly journals Selecting critical features for data classification based on machine learning methods

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Rung-Ching Chen ◽  
Christine Dewi ◽  
Su-Wen Huang ◽  
Rezzy Eko Caraka
Keyword(s):  
Machine Learning ◽  
Data Classification ◽  
Learning Methods ◽  
Machine Learning Methods

scholarly journals Predicting Breast Cancer Using Logistic Regression and Multi-Class Classifiers

2018 ◽  
Vol 7 (4.20) ◽  
pp. 22 ◽  
Author(s):  
Jabeen Sultana ◽  
Abdul Khader Jilani ◽  
. .
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Logistic Regression ◽  
Regression Method ◽  
Breast Cancer Dataset ◽  
Breast Cancer Data ◽  
Data Set ◽  
Cancer Data ◽  
Logistic Regression Method ◽  
Simple Logistic

The primary identification and prediction of type of the cancer ought to develop a compulsion in cancer study, in order to assist and supervise the patients. The significance of classifying cancer patients into high or low risk clusters needs commanded many investigation teams, from the biomedical and the bioinformatics area, to learn and analyze the application of machine learning (ML) approaches. Logistic Regression method and Multi-classifiers has been proposed to predict the breast cancer. To produce deep predictions in a new environment on the breast cancer data. This paper explores the different data mining approaches using Classification which can be applied on Breast Cancer data to build deep predictions. Besides this, this study predicts the best Model yielding high performance by evaluating dataset on various classifiers. In this paper Breast cancer dataset is collected from the UCI machine learning repository has 569 instances with 31 attributes. Data set is pre-processed first and fed to various classifiers like Simple Logistic-regression method, IBK, K-star, Multi-Layer Perceptron (MLP), Random Forest, Decision table, Decision Trees (DT), PART, Multi-Class Classifiers and REP Tree.  10-fold cross validation is applied, training is performed so that new Models are developed and tested. The results obtained are evaluated on various parameters like Accuracy, RMSE Error, Sensitivity, Specificity, F-Measure, ROC Curve Area and Kappa statistic and time taken to build the model. Result analysis reveals that among all the classifiers Simple Logistic Regression yields the deep predictions and obtains the best model yielding high and accurate results followed by other methods IBK: Nearest Neighbor Classifier, K-Star: instance-based Classifier, MLP- Neural network. Other Methods obtained less accuracy in comparison with Logistic regression method.  

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