Diagnóstico de cáncer de mama usando el tamaño del efecto d de Cohen como selector de características

Abstract

Breast cancer is a tumor that begins to grow in the milk ducts or lobules and can become lethal if
treatment is not administered in time. According to the World Health Organization (WHO), there were approximately
2.3 million cases of breast cancer in 2020. Furthermore, breast cancer can affect anyone, particularly
women over 50 years old. Therefore, it is crucial to have early diagnostic techniques. We propose a novel method
based on Cohen’s d for feature selection in this context. Cohen’s d is a statistical concept that quantifies the
strength of the relationship between two populations on a numeric scale. The central idea is to utilize Cohen’s d
effect size as a feature selector to reduce the dimensionality of the data and enhance the predictors through a Machine
Learning (ML) classifier model for diagnosing breast cancer. For experimental purposes, the Breast Cancer
Wisconsin database was used. This proposed feature selector is compared with two classical methods: Learning
Vector Quantization (LVQ) and Recursive Feature Elimination (RFE). A random evaluation of the features of
each selector was conducted 100 times using a Support Vector Machine (SVM) classifier, resulting in the following
average outcomes: Cohen’s d based feature selector showed 0.96 sensitivity and 0.97 specificity, RFE based
feature selector exhibited 0.95 sensitivity and 0.98 specificity, and LVQ based feature selector demonstrated 0.91
sensitivity and 0.96 specificity. These promising results indicate that the proposed methodology utilizing Cohen’s
d may be a valuable feature selector and sheds light on the long-standing research into breast cancer detection.