Comparative Analysis of Artificial Intelligence-Based Algorithms for Ear-ly Detection of Breast Cancer

Abstract

Breast cancer is one of the leading causes of mortality among women worldwide, representing a major public health problem. Early detection is crucial for increasing survival rates, since establishing a timely diagnosis and applying the appropriate treatment can help stop the disease in its early stages. In this context, the use of algorithms based on artificial intelligence has become a highly relevant tool in the field of early diagnosis, particularly within medical oncology. Several algorithms have demonstrated great potential in accurately classifying medical images, including Support Vector Machines (SVM), Random Forests, Multilayer Perceptrons (MLP), and Convolutional Neural Networks (CNN). This approach enables a more detailed and reliable analysis of medical imaging studies (mammograms, ultrasounds, computed tomography, and magnetic resonance imaging), facilitating the early detection of malignant lesions and reducing diagnostic errors. The main objective of this research was to conduct a comparative analysis of artificial intelligence-based algorithms and evaluate, through metrics such as accuracy, sensitivity, specificity, F1-score, and the area under the curve (AUC), which algorithm performs best in the early detection of the disease. Consequently, the integration of artificial intelligence algorithms into clinical decision support systems contributes significantly to improving medical decision-making, ensuring that treatments are more appropriate, and providing a better quality of life for patients diagnosed with the disease.