Using Supervised Learning for Breast Cancer Detection using AI&ML

Breast cancer is the leading cause of cancer-related deaths among women worldwide. A significant amount of research has been conducted to improve early detection of breast cancer, which is crucial for effective treatment and increased chances of survival. While mammograms have been the most reliable detection method, there is a need to explore alternatives that are cost-effective, safe, and accurate across different datasets. A hybrid paradigm of machine learning methods is presented in this work, proposed for effective breast cancer detection. The model combines several machine learning algorithms, including ANN, SVM, KNN and Decision Tree (DT), and can be applied to various data types, including images and blood tests. The proposed model aims to provide accurate results that are close to perfection.The presence or absence of micro-metastases plays a significant role in determining the fate of breast cancer. The proposed breast cancer detection machine utilizes machine learning and artificial intelligence techniques to identify breast cancer. The machine applies coding and techniques to detect breast cancer and project the results of different techniques. Depending on the need, the appropriate technique can be applied. This research focuses on predicting accuracy, and future research can explore other parameters to categorize breast cancer research further. Breast cancer detection remains a critical area of research, and the proposed hybrid model can be useful in providing accurate and cost-effective results for effective treatment and increased survival rates.