Breast Cancer Prediction: A Comparative Study Using Machine Learning Techniques

• Published in: SN computer science Vol. 1; no. 5; p. 290
• Main Authors: Islam, Md. Milon, Haque, Md. Rezwanul, Iqbal, Hasib, Hasan, Md. Munirul, Hasan, Mahmudul, Kabir, Muhammad Nomani
• Format: Journal Article
• Language: English
• Published: Singapore Springer Singapore 01.09.2020; Springer Nature B.V
• Subjects: Accuracy; Advances in Computational Approaches for Artificial Intelligence; Artificial neural networks; Breast cancer; Classification; Comparative studies; Computer Imaging; Computer Science; Computer Systems Organization and Communication Networks; Correlation coefficients; Data analysis; Data Structures and Information Theory; Datasets; Death; Decision trees; Disease; Feature selection; Image Processing, IoT and Cloud Applications; Information Systems and Communication Service; Machine learning; Mammography; Medical research; Original Research; Pattern Recognition and Graphics; Population growth; Software Engineering/Programming and Operating Systems; Sound waves; Supervised learning; Support vector machines; Vision; Womens health; United States > US; Logistic regression; Machine learning; Artificial neural networks; K-nearest neighbors; Breast cancer prediction; Support vector machine; Cancer dataset; Random forests
• ISSN: 2662-995X; 2661-8907
• DOI: 10.1007/s42979-020-00305-w

Early detection of disease has become a crucial problem due to rapid population growth in medical research in recent times. With the rapid population growth, the risk of death incurred by breast cancer is rising exponentially. Breast cancer is the second most severe cancer among all of the cancers already unveiled. An automatic disease detection system aids medical staffs in disease diagnosis and offers reliable, effective, and rapid response as well as decreases the risk of death. In this paper, we compare five supervised machine learning techniques named support vector machine (SVM), K-nearest neighbors, random forests, artificial neural networks (ANNs) and logistic regression. The Wisconsin Breast Cancer dataset is obtained from a prominent machine learning database named UCI machine learning database. The performance of the study is measured with respect to accuracy, sensitivity, specificity, precision, negative predictive value, false-negative rate, false-positive rate, F1 score, and Matthews Correlation Coefficient. Additionally, these techniques were appraised on precision–recall area under curve and receiver operating characteristic curve. The results reveal that the ANNs obtained the highest accuracy, precision, and F1 score of 98.57%, 97.82%, and 0.9890, respectively, whereas 97.14%, 95.65%, and 0.9777 accuracy, precision, and F1 score are obtained by SVM, respectively.