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

翻译：尽管已投入巨大努力，乳腺癌检测仍是一个开放的研究领域。效应量是一种统计概念，用于量化两个变量之间关系的强度。特征选择被广泛用于通过仅选取预测变量的子集来降低数据维度，从而改进学习模型。本研究通过算法和实验结果证明，利用从细胞核图像提取特征的参数化效应量度量来开发基于统计特征选择器的学习工具是可行的，该工具能够有效降低数据维度。采用线性核的SVM分类器作为学习工具，其准确率超过90%。这些优异结果表明效应量方法符合特征选择器方法的标准。