A Novel Ensemble Bagging Classification Method for Breast Cancer Classification Using Machine Learning Techniques

Abstract

Breast cancer is observed as a dangerous disease type for women in the world. The clinical experts stated that early detection of cancer helps in saving lives. To detect cancer in the early stage, medical image processing is observed as an effective field. Medical Image processing with an appropriate classification mechanism improves accuracy and image resource with minimal processing time. To detect breast cancer several machine learning techniques are evolved for cancer classification. However, those machine learning techniques are subjected to increased time consumption and limitation in the accuracy of classification. This paper proposed an Ensemble Bagging Weighted Voting Classification (EBWvc) for the classification of breast cancer. Initially, to resolve to overfit in machine learning bagging is applied for collected data. The ensemble bagging classification provides effective training to machine learning for reduced computational time and improved performance characteristics. The weighted voting is adopted for the classification of cancer in the breast. The performance of proposed EBWvc is analyzed comparatively with consideration accuracy, precision, recall, and F1 -Score. The comparative analysis of results exhibited that proposed EBWvc exhibits improved performance than existing classification techniques.

Early Identification of Risk Factors in Non-Alcoholic Fatty Liver Disease (NAFLD) Using Machine Learning

“…Next, the researchers found that a bagging ensemble with attribute selection could be an applicable method to support software tools for predicting the efficient results of schizophrenia. Ponnaganti and Anitha [9] suggested an Ensemble Bagging Weighted Voting Classification (EBWvc) method for the categorization of breast cancer. The researchers comparatively evaluated five metrics, and their results showed an increased performance of the EBWvc method if compared to similar existing classification methods.…”

A Hybrid Model Particle Swarm Optimization Based Mammogram Classification Using Kernel Support Vector Machine

“…The real input mammogram images were obtained from the universal available Image Analysis Society's database (MIAS). After the noise and artifact sources had been removed, the pectoral part of muscles from the image was removed by the seed growing technique such as contour growing, histogram thresholding and edge detection [7][8][9]. The defined method is perfect and completely computerized.…”

“…Almost all medical/diagnostic centers, however, are currently struggling to analyze the increasing volume of mammograms. Computer Aided Diagnosis (CAD) is more popular in diagnosing suspicious tumors in women's breasts with good results in terms of improved precision and accuracy [5][6][7][8][9].…”

“…A lot of strategies like Convolutional neural networks, LDA-Linear discriminate analysis and classification techniques have been used. Finally, Support Vector Machine is used to classify the features [7][8][9]16].…”

Breast Tumor Classification using Machine Learning

“…Their main goal was to aid in minimizing mistakes made when determining whether a breast tumour was malignant or not. For the classification of breast cancer, the paper [17] suggested an Ensemble Bagging Weighted Voting Classification (EBWvc). Initially, bagging is used for the obtained data to address overfit in machine learning.…”