Authors: G. Magesh; P. Swarnalatha
Addresses: School of Information Technology and Engineering, VIT University, Vellore, India ' School of Computer Science and Engineering, VIT University, Vellore, India
Abstract: Breast cancer is one of the most commonly occurring malignancies cancer in women, and there are millions of new cases diagnosed among women's and over 400,000 deaths annually worldwide. In our dataset, we have 30 real-valued attributes as features which are computed from the fine needle aspirate (FNA) test. Our dataset values are calculated from the processed image of a first needle aspirate test of a breast mass. Our input values are extracted from the digitalised image of the FNA test. There are many algorithms used for prediction systems. We are choosing the best algorithms based on the precision result, accuracy, and error rate. We are making a comparison of an effective way of applying algorithms and classifying data. We have different machine learning algorithms, a performance comparison conducted between those algorithms on the breast cancer datasets. Data visualisation and descriptive statistics have been presented. SVM with all features achieves 95% of precision, recall, and F1-score. After tuning the SVM parameters, accuracy has improved to 97%.
Keywords: breast cancer; machine learning; decision tree; classification; support vector machine; SVM; prediction.
International Journal of Cloud Computing, 2022 Vol.11 No.1, pp.43 - 60
Received: 06 Jun 2019
Accepted: 07 Oct 2019
Published online: 18 Feb 2022 *