Breast cancer is a prevalent form of cancer that affects a significant number of individuals worldwide and can have severe consequences if not detected and treated early. The World Health Organization (WHO) estimates that breast cancer is the most common cancer among women globally, with an estimated 2.3 million new cases in 2020 alone. Early detection is crucial in improving survival rates and treatment outcomes. This paper explores the application of Machine Learning (ML) techniques for predicting breast cancer diagnosis in individuals. We utilize a publicly available dataset from the Kaggle machine learning repository, which contains data from breast cancer patients collected from various medical institutions. Several machine learning models, including Naive Bayes Algorithm, Decision Trees, Logistic Regression, Neural Networks, Random Forest, Stochastic Gradient, and Support Vector Machines, are employed to analyze the dataset. The performance of these models is assessed using 10-fold cross-validation. Furthermore, we propose the most suitable machine learning algorithm for breast cancer diagnosis based on specified input parameters and discuss the potential deployment of a breast cancer diagnostic tool.

Keywords : Breast Cancer Detection, Supervised and Unsupervised Machine Learning, Artificial Intelligence.