An effective approach for breast cancer diagnosis based on routine blood analysis features

• Published in: Medical & biological engineering & computing Vol. 58; no. 7; pp. 1583 - 1601
• Main Authors: Yavuz, Erdem, Eyupoglu, Can
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.07.2020; Springer Nature B.V
• Subjects: Algorithms; Anthropometry; Artificial neural networks; Biomedical and Life Sciences; Biomedical Engineering and Bioengineering; Biomedicine; Blood; Blood tests; Breast cancer; Breast Neoplasms - blood; Breast Neoplasms - diagnosis; Computer Applications; Databases, Factual; Datasets; Diagnosis; Diagnosis, Computer-Assisted - methods; Female; Human Physiology; Humans; Imaging; Iterative methods; Medical diagnosis; Neural networks; Neural Networks, Computer; Original Article; Physicians; Prediction models; Principal Component Analysis; Principal components analysis; Radiology; Regression analysis; Regression models; Software; Training; Routine blood analysis; GRNN; Breast cancer diagnosis; Classification; Median filtering; PCA
• ISSN: 0140-0118; 1741-0444; 1741-0444
• DOI: 10.1007/s11517-020-02187-9

Breast cancer is a widespread disease and one of the primary causes of cancer mortality among women all over the world. Computer-aided methods are used to assist medical doctors to make early diagnosis of the disease. The aim of this study is to build an effective prediction model for breast cancer diagnosis based on anthropometric data and parameters collected through routine blood analysis. The proposed approach innovatively exploits principal component analysis (PCA) technique cascaded by median filtering so as to transform original features into a form of containing less distractive noise not to cause overfitting. Since a generalized regression neural network (GRNN) model is adopted to classify patterns of the transformed features, the computational load imposed in the training of artificial neural network model is kept minimized thanks to the non-iterative nature of GRNN training. The proposed method has been devised and tested on the recent Breast Cancer Coimbra Dataset (BCCD) that contains 9 clinical features measured for each of 116 subjects. Outperforming all of the existing studies on BCCD, our method achieved a mean accuracy rate of 0.9773. Experimental results evidence that this study achieves the best prediction performance ever reported on this dataset. The fact that our proposed approach has accomplished such a boosted performance of breast cancer diagnosis based on routine blood analysis features offers a great potential to be used in a widespread manner to detect the disease in its inception phase. Graphical abstract