A Comparative Analysis of Breast Cancer Data Set Using Different Classification Methods

2018 ◽  
pp. 175-181 ◽  
Author(s):  
M. Navya Sri ◽  
J. S. V. S. Hari Priyanka ◽  
D. Sailaja ◽  
M. Ramakrishna Murthy
Keyword(s):  
Breast Cancer ◽  
Comparative Analysis ◽  
Classification Methods ◽  
Breast Cancer Data ◽  
Data Set ◽  
Cancer Data

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.  

scholarly journals Using new artificial bee colony as probabilistic neural network for breast cancer data classification

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Habib Shah
Keyword(s):  
Breast Cancer ◽  
Neural Network ◽  
Artificial Bee Colony ◽  
Probabilistic Neural Network ◽  
Breast Cancers ◽  
Breast Cancer Data ◽  
Data Set ◽  
Content Type ◽  
Cancer Data ◽  
Bee Colony

PurposeBreast cancer is an important medical disorder, which is not a single disease but a cluster more than 200 different serious medical complications.Design/methodology/approachThe new artificial bee colony (ABC) implementation has been applied to probabilistic neural network (PNN) for training and testing purpose to classify the breast cancer data set.FindingsThe new ABC algorithm along with PNN has been successfully applied to breast cancers data set for prediction purpose with minimum iteration consuming.Originality/valueThe new implementation of ABC along PNN can be easily applied to times series problems for accurate prediction or classification.

scholarly journals Research of Feature Selection Methods to Predict Breast Cancer

2019 ◽  
Vol 8 (2S11) ◽  
pp. 2353-2355 ◽  
Keyword(s):  
Breast Cancer ◽  
Feature Selection ◽  
Early Stage ◽  
Classification Methods ◽  
Selection Methods ◽  
Breast Cancer Data ◽  
Cancer Data ◽  
Cancer Breast ◽  
Data Mining Approach ◽  
The World

Human health is most important than anything in the world, one should take care of it. Among various disease, cancer is the most terrible and deadly disease, so it is necessary to predict such disease in early stage. In this paper different feature selection methods used for feature extraction with different feature classification methods to identify the breast cancer. Breast cancer data is taken from UCI repository and is processed using WEKA tool and proposed techniques are applied to classify data accurately. This study well defines that data mining approach is suitable for predicting breast cancer.

A Markov chain model of a longitudinal breast cancer data set.

2014 ◽  
Vol 32 (15_suppl) ◽  
pp. 11040-11040
Author(s):  
Paul K. Newton ◽  
Jorge J. Nieva ◽  
Peter Kuhn ◽  
Larry Norton ◽  
Elizabeth Anne Comen ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Markov Chain ◽  
Markov Chain Model ◽  
Chain Model ◽  
Breast Cancer Data ◽  
Data Set ◽  
Cancer Data

scholarly journals Synthesising artificial patient-level data for Open Science - an evaluation of five methods

2020 ◽  
Author(s):  
Michael Allen ◽  
Andrew Salmon
Keyword(s):  
Breast Cancer ◽  
Logistic Regression ◽  
Synthetic Data ◽  
Original Data ◽  
Classification Model ◽  
Data Sets ◽  
List Type ◽  
Breast Cancer Data ◽  
Data Set ◽  
Cancer Data

ABSTRACTBackgroundOpen science is a movement seeking to make scientific research accessible to all, including publication of code and data. Publishing patient-level data may, however, compromise the confidentiality of that data if there is any significant risk that data may later be associated with individuals. Use of synthetic data offers the potential to be able to release data that may be used to evaluate methods or perform preliminary research without risk to patient confidentiality.MethodsWe have tested five synthetic data methods:A technique based on Principal Component Analysis (PCA) which samples data from distributions derived from the transformed data.Synthetic Minority Oversampling Technique, SMOTE which is based on interpolation between near neighbours.Generative Adversarial Network, GAN, an artificial neural network approach with competing networks - a discriminator network trained to distinguish between synthetic and real data., and a generator network trained to produce data that can fool the discriminator network.CT-GAN, a refinement of GANs specifically for the production of structured tabular synthetic data.Variational Auto Encoders, VAE, a method of encoding data in a reduced number of dimensions, and sampling from distributions based on the encoded dimensions.Two data sets are used to evaluate the methods:The Wisconsin Breast Cancer data set, a histology data set where all features are continuous variables.A stroke thrombolysis pathway data set, a data set describing characteristics for patients where a decision is made whether to treat with clot-busting medication. Features are mostly categorical, binary, or integers.Methods are evaluated in three ways:The ability of synthetic data to train a logistic regression classification model.A comparison of means and standard deviations between original and synthetic data.A comparison of covariance between features in the original and synthetic data.ResultsUsing the Wisconsin Breast Cancer data set, the original data gave 98% accuracy in a logistic regression classification model. Synthetic data sets gave between 93% and 99% accuracy. Performance (best to worst) was SMOTE > PCA > GAN > CT-GAN = VAE. All methods produced a high accuracy in reproducing original data means and stabdard deviations (all R-square > 0.96 for all methods and data classes). CT-GAN and VAE suffered a significant loss of covariance between features in the synthetic data sets.Using the Stroke Pathway data set, the original data gave 82% accuracy in a logistic regression classification model. Synthetic data sets gave between 66% and 82% accuracy. Performance (best to worst) was SMOTE > PCA > CT-GAN > GAN > VAE. CT-GAN and VAE suffered loss of covariance between features in the synthetic data sets, though less pronounced than with the Wisconsin Breast Cancer data set.ConclusionsThe pilot work described here shows, as proof of concept, that synthetic data may be produced, which is of sufficient quality to publish with open methodology, to allow people to better understand and test methodology. The quality of the synthetic data also gives promise of data sets that may be used for screening of ideas, or for research project (perhaps especially in an education setting).More work is required to further refine and test methods across a broader range of patient-level data sets.

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.

scholarly journals A new generalization of Weibull distribution with application to a breast cancer data set

2009 ◽  
Vol 28 (16) ◽  
pp. 2077-2094 ◽  
Author(s):  
Abdus S. Wahed ◽  
The Minh Luong ◽  
Jong-Hyeon Jeong
Keyword(s):  
Breast Cancer ◽  
Weibull Distribution ◽  
Breast Cancer Data ◽  
Data Set ◽  
Cancer Data
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