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Breast Cancer Data Classification Using Ensemble Machine Learning |
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| รหัสดีโอไอ | |
| Creator | Meerja Akhil Jabbar |
| Title | Breast Cancer Data Classification Using Ensemble Machine Learning |
| Publisher | Faculty of Engineering, Khon Kaen University |
| Publication Year | 2564 |
| Journal Title | Engineering and Applied Science Research |
| Journal Vol. | 48 |
| Journal No. | 1 |
| Page no. | 65-72 |
| Keyword | Breast cancer, Ensemble learning, Machine learning, Bayesian network, Radial basis function, Classification, Wisconsin breast cancer data set, Accuracy |
| URL Website | https://www.tci-thaijo.org/index.php/easr/index |
| Website title | Engineering and Applied Science Research |
| ISSN | 2539-6161 |
| Abstract | Breast cancer (BC) is the largest cause of death in women. Accurate classification of breast cancer data is important in cancer diagnosis and classification of Malignant and Benign tumors can prevent patients to take unnecessary tests. Breast cancer classification can also be used to determine suitable treatment. Classification of Benign and Malignant patient groups is widely recognized research in the medical field. Due to the advantage of detecting critical features from a medical data set, machine learning is widely used in Breast cancer Prediction. Recently there has been greater attention to the use of machine learning methods in medical diagnosis. These decision support systems are effective and helpful for medical experts in the healthcare domain. The objective of this work is to address the problem of the classification of breast cancer data using ensemble learning. Ensemble learning techniques are used to improve the performance of a classifier. This paper deals with building a decision support system using the ensemble model built with Bayesian network and Radial Basis Function. In this work, extensive experiments were carried out on the much- studied open access data set "Wisconsin Breast Cancer Data set (WBCD)". The data set is partitioned into training and testing. Various metrics like accuracy, sensitivity, specificity, positive predictive value, negative predicted value, Error rate, false-positive rate, Mathew's correlation coefficient were used to measure the performance of the model. Experimental results show that the proposed method records a remarkable accuracy of 97% to classify breast cancer data and outperformed the existing approaches. The proposed ensemble learning would be viable in helping cancer specialists in recognizing cancer tumors accurately and help the patients in taking the correct treatment. |
