Enhancing Early Detection and Prognosis of Breast Cancer Through Advanced Machine Learning Techniques A Comprehensive Predictive Modeling Approach

Abstract

This study makes use of a publicly available dataset for breast tumor prediction in order to enhance early identification and evaluation via machine learning. Using Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), and Artificial Neural Networks (ANN), we create and assess predictive models for precise breast cancer classification. These models are trained, verified, and tested using the dataset's primary clinical and diagnostic characteristics. The efficacy of each strategy is compared through the analysis of performance indicators like accuracy, precision, recall, and F1-score. Superior results from CNN and ANN showed how machine learning has a lot of potential for accurate breast cancer diagnosis. This study emphasizes how crucial predictive modeling is to improving diagnostic precision and assisting physicians in making better decisions for their patients.