Effect sizes as a statistical feature-selector-based learning to detect breast cancer
This work addresses breast cancer detection, a critical medical problem, but appears incremental as it applies existing feature selection methods with effect size to a known dataset.
The authors tackled breast cancer detection by developing a statistical feature-selector-based learning tool that uses effect size measures to reduce data dimensionality from cell nuclei images, achieving over 90% accuracy with an SVM classifier.
Breast cancer detection is still an open research field, despite a tremendous effort devoted to work in this area. Effect size is a statistical concept that measures the strength of the relationship between two variables on a numeric scale. Feature selection is widely used to reduce the dimensionality of data by selecting only a subset of predictor variables to improve a learning model. In this work, an algorithm and experimental results demonstrate the feasibility of developing a statistical feature-selector-based learning tool capable of reducing the data dimensionality using parametric effect size measures from features extracted from cell nuclei images. The SVM classifier with a linear kernel as a learning tool achieved an accuracy of over 90%. These excellent results suggest that the effect size is within the standards of the feature-selector methods