$\text{O}^2$PF: Oversampling via Optimum-Path Forest for Breast Cancer Detection

TL;DR

This paper introduces $ ext{O}^2$PF, an oversampling technique based on the Optimum-Path Forest algorithm, to improve breast cancer detection accuracy by addressing data imbalance issues in machine learning models.

Contribution

The paper presents a novel oversampling method using Optimum-Path Forest, specifically designed for imbalanced medical datasets like breast cancer detection.

Findings

$ ext{O}^2$PF outperforms three established oversampling methods.

The method demonstrates robustness across multiple breast cancer datasets.

Improves classifier performance in imbalanced medical data scenarios.

Abstract

Breast cancer is among the most deadly diseases, distressing mostly women worldwide. Although traditional methods for detection have presented themselves as valid for the task, they still commonly present low accuracies and demand considerable time and effort from professionals. Therefore, a computer-aided diagnosis (CAD) system capable of providing early detection becomes hugely desirable. In the last decade, machine learning-based techniques have been of paramount importance in this context, since they are capable of extracting essential information from data and reasoning about it. However, such approaches still suffer from imbalanced data, specifically on medical issues, where the number of healthy people samples is, in general, considerably higher than the number of patients. Therefore this paper proposes the $\text{O}^2$PF, a data oversampling method based on the unsupervised Optimum-Path Forest Algorithm. Experiments conducted over the full oversampling scenario state the robustness of the model, which is compared against three well-established oversampling methods considering three breast cancer and three general-purpose tasks for medical issues datasets.