Research on Prediction Model of Benign and Malignant Breast Cancer Based on Machine Learning

Abstract

This study uses three machine learning models to perform classification analysis on breast cancer data, aiming to improve diagnostic accuracy. After data preprocessing and standardization, the performance of each model was comprehensively evaluated using classification and regression metrics. The results showed that the logistic regression model performed the best, with an accuracy of 0.9825, while SVM and random forest also showed good performance. The classification effects of each model were visualized through ROC curves and confusion matrices, demonstrating that logistic regression has high application value in breast cancer diagnosis.