A Study of Breast Cancer Prediction Using Machine Learning

Improved survival rates and effective treatment depend on early and precise diagnosis.In order to create a prediction model for the classification of breast cancer, this study investigates the use of many machine learning (ML) techniques.We trained and assessed Random Forest and Logistic Regression classifiers using the publicly accessible Wisconsin Breast Cancer Diagnostic (WBCD) dataset.Accuracy, precision, recall, and F1-score were used to evaluate the models.Our findings show that the logistic regression is a useful tool for differentiating between benign and malignant tumors.According to the study's findings, machine learning offers a solid and trustworthy method for predicting breast cancer, which can help medical practitioners make better decisions and cut down on needless biopsies.