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

arXiv: https://arxiv.org/abs/2411.06868 · HTML: https://mlsanity.com/paper/2411.06868/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 23 references that are in the map.
- First posted on arXiv 11 Nov 2024.

## Builds on (most cited first, 2 of 2)

- [Relief-Based Feature Selection: Introduction and Review](https://mlsanity.com/paper/1711.08421/) (2017)
- [Survey on Feature Selection](https://mlsanity.com/paper/1510.02892/) (2015)

## Built on by (most cited first, 1 of 1)

- [Hybrid Pipeline SWD Detection in Long-Term EEG Signals](https://mlsanity.com/paper/2509.19387/) (2025)

## Abstract (arXiv)

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