Article citationsMore>>

Jiang, Y., Nishikawa, R., Wolverton, D., Metz, C., Giger, M.L., Schmidt, R. and Doi, K. (1996) Malignant and Benign Clustered Microcalcifications: Automated Feature Analysis and Classification. Radiology, 198, 671-678.
http://dx.doi.org/10.1148/radiology.198.3.8628853

has been cited by the following article:

• TITLE: Comparative Analysis of Different Classifiers for the Wisconsin Breast Cancer Dataset

  AUTHORS: Leena Vig

  KEYWORDS: WBCD, Machine Learning, Classification, Naive Bayes, Neural Networks, SVM

  JOURNAL NAME: Open Access Library Journal, Vol.1 No.6, September 24, 2014

  ABSTRACT: The Wisconsin Breast Cancer Dataset has been heavily cited as a benchmark dataset for classification. Neural Network techniques such as Neural Networks, Probabilistic Neural Networks, and Regression Neural Networks have been shown to perform very well on this dataset. However, despite its obvious practical importance and implications for cancer research, a thorough investigation of all modern classification techniques on this dataset remains to be done. In this paper we examine the efficacy of classifiers such as Random Forests with varying number of trees, Support Vector Machines with different kernels, Naive Bayes model and neural networks on the accuracy of classifying the masses in the dataset as benign/malignant. Results indicate that Support Vector machines with a Radial Basis function kernel give the best accuracy of all the models attempted. This indicates that there are non-linearities present in the dataset and that the Support vector machine does a good job of mapping the data into a higher dimensional space in which the non-linearities fade away and the data becomes linearly separable by large margin classifier like the support vector machine. These methods show that modern machine learning methods could provide for improved accuracy for early prediction of cancerous tumors.