Abstract
The Biofield Diagnostic System (BDS) is an adjunct breast cancer detection modality that uses recorded skin surface electropotentials for differentiating benign and malignant lesions. The main objective of this paper is to apply data mining techniques to two BDS clinical trial datasets to improve the disease detection accuracy. Both the datasets are pre-processed to remove outliers and are then used for feature selection. Wrapper and filter feature selection techniques are employed and the selected features are used for classification using supervised techniques like Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis (QDA), Support Vector Machines (SVM) and Back Propagation Neural Network (BPNN). It was observed that the LDA classifier using the feature subset selected via wrapper technique significantly improved the sensitivity and accuracy of one of the datasets. Also, the key observation is that this feature subset reduced the mild subjective interpretation associated with the current prediction methodology of the BDS device, thereby opening up new development avenues for the BDS device.
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Sree, S.V., Ng, E.Y.K., Kaw, G., Acharya, U.R. (2009). Use of Data Mining Techniques for Improved Detection of Breast Cancer with Biofield Diagnostic System. In: Palmer-Brown, D., Draganova, C., Pimenidis, E., Mouratidis, H. (eds) Engineering Applications of Neural Networks. EANN 2009. Communications in Computer and Information Science, vol 43. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03969-0_42
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DOI: https://doi.org/10.1007/978-3-642-03969-0_42
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