Machine Learning-Based Predictive Models for Breast Cancer Recurrence
Keywords:
breast cancer, machine learning, disease recurrence, prediction modelAbstract
Introduction: Breast cancer is the most common malignant neoplasm in women worldwide. The prevalence and incidence of breast cancer increase every year; therefore, early diagnosis, along with adequate detection of recurrence, is an important strategy to improve prognosis.
Objective: This study aimed to compare different machine learning algorithms to select the best predictive model for breast cancer recurrence.
Method: A prospective longitudinal analytical study was conducted in which several machine learning models were developed, including logistic regression, random forest, linear discriminant analysis, and AdaBoost to predict breast cancer recurrence. The area under the curve, accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value, and F1 score were used to evaluate the performance of the prognostic model. Results: The variables triple-negative, Ki-67, vascular and lymphatic permeation, and lymphadenopathy accounted for 78% of predictive importance. Random forest and AdaBoost models showed the best overall performance in terms of area under the curve, sensitivity, specificity, and F1 score, detecting 34 out of 37 recurrences.
Conclusions: Based on performance, the models demonstrated excellent stability and generalization, with minimal performance drops corrected under clinical supervision.
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Copyright (c) 2026 Rene Borges Sandrino, Enia Ramon Musibay, Vivian Sistachs-Vega, Miguel Ángel Díaz Martínez , Mercedes Gisela Borges Sandrino
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