Breast Cancer Detection: A Comprehensive Study on Machine Learning and Deep Learning Techniques

Breast cancer is among the most common cancers affecting women globally. Early detection is crucial in reducing mortality rates and improving treatment outcomes. This project utilizes machine learning to develop a breast cancer detection model based on patient medical data. The Random Forest Classifier was selected due to its high accuracy and capacity to handle imbalanced datasets. The project also integrates a frontend interface that allows users to input relevant data and find nearby cancer treatment centers through a location-based service. With an accuracy of over 95%, the model offers a promising tool to assist healthcare professionals and patients. Future improvements aim to enhance the dataset and user accessibility, making it a more versatile and scalable solution.