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

• URL: http://arxiv.org/abs/2411.06868v1
• Date: Mon, 11 Nov 2024 11:07:38 GMT
• Title: Effect sizes as a statistical feature-selector-based learning to detect breast cancer
• Authors: Nicolas Masino, Antonio Quintero-Rincon,
• Abstract summary: Effect size is a statistical concept that measures the strength of the relationship between two variables on a numeric scale. In this work, an algorithm and experimental results demonstrate the feasibility of developing a statistical feature-selector-based learning tool.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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

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