scholarly journals Deep Learning Hybrid with Binary Dragonfly Feature Selection for the Wisconsin Breast Cancer Dataset

2021 ◽  
Vol 12 (3) ◽  
Author(s):  
Marian Mamdouh Ibrahim ◽  
Dina Ahmed ◽  
Rania Ahmed
Keyword(s):  
Breast Cancer ◽  
Feature Selection ◽  
Deep Learning ◽  
Breast Cancer Dataset ◽  
Cancer Dataset ◽  
Selection For

scholarly journals Deep Convolution Neural Network Based Breast Cancer Bigdata Analysis for Crowd Cloud Sourcing

2020 ◽  
Vol 9 (3S) ◽  
pp. 378-383
Keyword(s):  
Breast Cancer ◽  
Deep Learning ◽  
Learning Algorithms ◽  
Medical Industry ◽  
Machine Learning Algorithms ◽  
The Other ◽  
Major Surgery ◽  
Breast Cancer Dataset ◽  
Cancer Dataset ◽  
Deep Convolution Neural Network

Breast cancer is one of the dangerous diseases leads fast death among women. Several kinds of cancers are affecting people, but breast cancer affects highly women. In medical industry removal of women breasts or major surgery is taken forward as the solution, where it reoccurs after surgery also. Only solution to save women from breast cancer is to identify and detect the earlier stage of cancer and provide necessary treatment. Hence various research works have been focused on finding good solution for diagnosing and classifying the cancer stages as benign, malignant or severe malignant. Still the accuracy of classification needs to be improved on complex breast cancer datasets. Few of the earlier research works have proposed machine learning algorithms, which are semiautomatic and accuracy is also not high. Thus, to provide a better solution this paper aimed to use one of the deep learning algorithms such as Convolution Neural Networks for diagnosing various kinds of breast cancer dataset. From the experimental results, it is obtained that the proposed deep learning algorithms outperforms than the other algorithms.

Predictive Modeling for Classification of Breast Cancer Dataset Using Feature Selection Techniques

2021 ◽  
pp. 204-215
Author(s):  
Leena Nesamani S. ◽  
S. Nirmala Sigirtha Rajini
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Feature Selection ◽  
Mutual Information ◽  
Predictive Modeling ◽  
Breast Cancer Dataset ◽  
Cancer Dataset ◽  
Chi Squared ◽  
Feature Selection Techniques

Predictive modeling or predict analysis is the process of trying to predict the outcome from data using machine learning models. The quality of the output predominantly depends on the quality of the data that is provided to the model. The process of selecting the best choice of input to a machine learning model depends on a variety of criteria and is referred to as feature engineering. The work is conducted to classify the breast cancer patients into either the recurrence or non-recurrence category. A categorical breast cancer dataset is used in this work from which the best set of features is selected to make accurate predictions. Two feature selection techniques, namely the chi-squared technique and the mutual information technique, have been used. The selected features were then used by the logistic regression model to make the final prediction. It was identified that the mutual information technique proved to be more efficient and produced higher accuracy in the predictions.

scholarly journals Exploring the performance of feature selection method using breast cancer dataset

2022 ◽  
Vol 25 (1) ◽  
pp. 232
Author(s):  
Tsehay Admassu Assegie ◽  
Ravulapalli Lakshmi Tulasi ◽  
Vadivel Elanangai ◽  
Napa Komal Kumar
Keyword(s):  
Breast Cancer ◽  
Feature Selection ◽  
Predictive Model ◽  
Feature Selection Method ◽  
Experimental Result ◽  
Breast Cancer Dataset ◽  
Chi Square ◽  
Cancer Dataset ◽  
Cancer Diagnostic ◽  
Sequential Feature Selection

Breast cancer is the most common type of cancer occurring mostly in females. In recent years, many researchers have devoted to automate diagnosis of breast cancer by developing different machine learning model. However, the quality and quantity of feature in breast cancer diagnostic dataset have significant effect on the accuracy and efficiency of predictive model. Feature selection is effective method for reducing the dimensionality and improving the accuracy of predictive model. The use of feature selection is to determine feature required for training model and to remove irrelevant and duplicate feature. Duplicate feature is a feature that is highly correlated to another feature. The objective of this study is to conduct experimental research on three different feature selection methods for breast cancer prediction. Sequential, embedded and chi-square feature selection are implemented using breast cancer diagnostic dataset. The study compares the performance of sequential embedded and chi-square feature selection on test set. The experimental result evidently shows that sequential feature selection outperforms as compared to chi-square (X<sup>2</sup>) statistics and embedded feature selection. Overall, sequential feature selection achieves better accuracy of 98.3% as compared to chi-square (X<sup>2</sup>) statistics and embedded feature selection.

scholarly journals Comparison of microarray breast cancer classification using support vector machine and logistic regression with LASSO and boruta feature selection

2020 ◽  
Vol 20 (2) ◽  
pp. 712
Author(s):  
Nursabillilah Mohd Ali ◽  
Nor Azlina Ab Aziz ◽  
Rosli Besar
Keyword(s):  
Breast Cancer ◽  
Feature Selection ◽  
Feature Selection Method ◽  
Diagnostic System ◽  
Cancer Classification ◽  
Support Vector ◽  
Breast Cancer Dataset ◽  
Breast Cancer Patients ◽  
Cancer Dataset ◽  
Breast Cancer Classification

<p>Breast cancer is the most frequent cancer diagnosis amongst women worldwide. Despite the advancement of medical diagnostic and prognostic tools for early detection and treatment of breast cancer patients, research on development of better and more reliable tools is still actively conducted globally. The breast cancer classification is significantly important in ensuring reliable diagnostic system. Preliminary research on the usage of machine learning classifier and feature selection method for breast cancer classification is conducted here. Two feature selection methods namely Boruta and LASSO and SVM and LR classifier are studied. A breast cancer dataset from GEO web is adopted in this study. The findings show that LASSO with LR gives the best accuracy using this dataset.</p>

scholarly journals Deep Learning Approach to Identifying Breast Cancer Subtypes Using High-Dimensional Genomic Data

2019 ◽  
Author(s):  
Runpu Chen ◽  
Le Yang ◽  
Steve Goodison ◽  
Yijun Sun
Keyword(s):  
Breast Cancer ◽  
Deep Learning ◽  
Cluster Structure ◽  
Data Representation ◽  
High Dimensional ◽  
Breast Cancer Dataset ◽  
Cancer Dataset ◽  
Cancer Subtypes ◽  
Novel Approach ◽  
Open Source Software Package

AbstractMotivationCancer subtype classification has the potential to significantly improve disease prognosis and develop individualized patient management. Existing methods are limited by their ability to handle extremely high-dimensional data and by the influence of misleading, irrelevant factors, resulting in ambiguous and overlapping subtypes.ResultsTo address the above issues, we proposed a novel approach to disentangling and eliminating irrelevant factors by leveraging the power of deep learning. Specifically, we designed a deep learning framework, referred to as DeepType, that performs joint supervised classification, unsupervised clustering and dimensionality reduction to learn cancer-relevant data representation with cluster structure. We applied DeepType to the METABRIC breast cancer dataset and compared its performance to state-of-the-art methods. DeepType significantly outperformed the existing methods, identifying more robust subtypes while using fewer genes. The new approach provides a framework for the derivation of more accurate and robust molecular cancer subtypes by using increasingly complex, multi-source data.Availability and implementationAn open-source software package for the proposed method is freely available atwww.acsu.buffalo.edu/~yijunsun/lab/DeepType.html.

Design of novel multi filter union feature selection framework for breast cancer dataset

2021 ◽  
pp. 1063293X2110160
Author(s):  
Dinesh Morkonda Gunasekaran ◽  
Prabha Dhandayudam
Keyword(s):  
Breast Cancer ◽  
Feature Selection ◽  
Care Center ◽  
Feature Selection Method ◽  
Selection Method ◽  
Cancer Center ◽  
Breast Cancer Dataset ◽  
Data Set ◽  
Health Care Center ◽  
Cancer Data

Nowadays women are commonly diagnosed with breast cancer. Feature based Selection method plays an important step while constructing a classification based framework. We have proposed Multi filter union (MFU) feature selection method for breast cancer data set. The feature selection process based on random forest algorithm and Logistic regression (LG) algorithm based union model is used for selecting important features in the dataset. The performance of the data analysis is evaluated using optimal features subset from selected dataset. The experiments are computed with data set of Wisconsin diagnostic breast cancer center and next the real data set from women health care center. The result of the proposed approach shows high performance and efficient when comparing with existing feature selection algorithms.

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