AI4BCancer: Breast Cancer Classification using AutoML - TPOT with PCA

Breast cancer is the most common cancer diagnosed in women and is a significant cause of cancer-related deaths among women worldwide. It demands accurate and timely detection methods for improved patient outcomes. This study proposes a novel machine learning approach to enhance breast cancer classification. Emphasizing the urgency of early diagnosis, we compare existing models and find AutoML - TPOT with PCA achieving the highest accuracy among all models tested. This proposed model integrates advanced algorithms and PCA to improve accuracy and computational efficiency. Through rigorous evaluation on the Wisconsin Breast Cancer Dataset, this model demonstrates superior performance, achieving an accuracy of 98.6%. This highlights its potential to significantly impact breast cancer diagnosis and treatment planning, contributing to ongoing efforts to improve patient care. Future work will focus on integrating Explainable AI to enhance model transparency and clinical trust.