Machine Learning-Based Breast Cancer Probability Prediction

Breast cancer, as the most common cancer among women globally, the key to improving its efficacy lies in early detection and diagnosis. Currently, breast cancer screening mainly relies on mammography, but this technology alone cannot accurately screen for breast cancer, leading to potential misdiagnoses. For breast cancer, the key to improving prognosis and survival rate is early detection. Additionally, breast cancer patients can significantly extend their survival time and improve their quality of life through good prognosis analysis. Therefore, this study focuses on the early diagnosis and prognosis analysis of breast cancer, applying machine learning and data analysis techniques combined with statistical analysis methods to analyze existing clinical data and achieve a high-accuracy disease analysis judgment for breast cancer. This study explores the effectiveness of applying various machine learning models in breast cancer prediction. This study used classic algorithms such as K-nearest neighbors, support vector machine, and Bayesian classifier, and conducted detailed data preprocessing and analysis. In this study, the breast cancer dataset was trained in different models respectively. The obtained evaluation results such as the prediction accuracy were compared to select the machine learning model with the best performance. It can be known from the final experiment that KNN model performs best on the dataset, followed by the SVM and KNN models. This study not only demonstrates the potential of machine learning in medical diagnosis but also provides important references for further optimizing breast cancer prediction models in the future.