Prognosis and Prediction of Breast Cancer Using Machine Learning and Ensemble-Based Training Model. (8th October 2021)

• Record Type: Journal Article
• Authors: Gupta, Niharika; Kaushik, Baij Nath
• Is Part Of: Computer journal. Volume 66:Number 1(2023)
• Page Start: 70
• Page End: 85
• Publication Date: 2021-10-08
• Subjects: breast cancer -- confusion matrix -- machine learning -- support vector machine -- random forest -- voting ensemble; Computers -- Periodicals; 005.1
• DOI: 10.1093/comjnl/bxab145
• Languages: English
• ISSNs: 0010-4620

Abstract

Abstract: There has been an increase in occurrence of human diseases all over the world. Among those, Breast Cancer has increased with an alarming rate in the past decade and this trend of increase would continue to grow. Now, there is a need for efficient text analytics and feature extraction tools to assist classifying, sharing and retrieving the information on human diseases in general and Breast Cancer in particular. In light of above, the present study has been undertaken with the objective to provide a comparative analysis of different classifiers on Breast Cancer dataset, and to propose a new ensemble training method of Machine Learning Classification. Here, machine learning models (such as K-Nearest Neighbour, Logistic Regression, Decision Tree, Random Forest, Gradient Boost, Support Vector Machine) and deep learning classifiers (such as Multi-Layer Feed Forward Neural Network, Recurrent Neural Network and Long Short Term Memory) have been applied on Breast Cancer dataset. An Ensemble Learning model for Prediction is proposed to classify the results among different classifiers. Finally, the Voting Ensemble is implemented to find out the optimal classifier for prediction of Breast Cancer. The results have been computed using the evaluation parameters such as Accuracy, Precision, Recall and Specificity. The confusion matrix drawn on the basis of evaluation parameters provides more emphasis on predicted and actual instances. Performance Evaluation for various machine learning models is computed. Results of this investigation concludes that Voting Ensemble outperforms other machine learning models. The prediction using Voting Ensemble resulted in an accuracy rate of 97.9 per cent, precision of 96.77 per cent and recall of 100 per cent.