Machine Learning in Breast Cancer Diagnosis: A Comprehensive Analysis and Prediction Methodology

Abstract

Breast cancer remains a significant global health concern, necessitating effective early detection and treatment strategies. In this paper, we conduct a detailed analysis of the application of machine learning techniques in healthcare, with a specific focus on breast cancer diagnosis. Through a systematic review of existing literature, we explore various approaches and methodologies used by researchers to leverage medical data for breast cancer prediction. Additionally, we present a comprehensive methodology for data collection, preprocessing, and analysis, employing machine learning algorithms such as logistic regression, random forests, and neural networks. Our results demonstrate the efficacy of machine learning models in accurately predicting breast cancer, with promising performances in terms of sensitivity, specificity, and precision. This study highlights the transformative potential of machine learning in enhancing breast cancer diagnosis and underscores its role in improving clinical outcomes and patient care.