A Machine Learning Approach to Early Detection and Malignancy Prediction in Breast Cancer

Abstract

Breast cancer is the most common cancer among women, making early detection crucial for effective treatment. Traditional diagnostic methods often face limitations, leading to potential errors in diagnosis. This study explores the transformative potential of artificial intelligence (AI) and machine learning (ML) in breast cancer diagnosis, particularly through models like AdaBoost, SVM, Random Forest, and logistic regression. By analyzing key variables—such as age, tumor size, and menopausal status—this research aims to accurately differentiate between malignant and benign lesions. The findings reveal that the AdaBoost model significantly outperforms others, achieving an impressive AUC of 93.60% and a precision rate of 95.65%. This indicates its exceptional ability to accurately classify cases, minimizing false positives and ensuring reliable detection of true positives. With an F1 score of 86.27%, AdaBoost effectively balances precision and recall, positioning it as a valuable tool in clinical settings. Overall, this study underscores the importance of integrating AI-driven approaches in breast cancer diagnosis, enhancing accuracy and improving patient outcomes while reducing unnecessary invasive procedures. The promising results advocate for the adoption of these advanced techniques in healthcare, paving the way for more personalized and effective treatment strategies.