Effect sizes as a statistical feature-selector-based learning to detect breast cancer

TL;DR

This paper proposes a statistical feature selection method based on effect size measures for breast cancer detection, demonstrating high accuracy with SVM classifiers on cell nuclei image data.

Contribution

It introduces a novel approach combining effect size-based feature selection with machine learning for improved breast cancer detection.

Findings

Achieved over 90% accuracy with SVM classifier.

Validated the effectiveness of effect size measures for feature selection.

Demonstrated feasibility of reducing data dimensionality in medical imaging.

Abstract

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