Analysis and Detection of Breast Cancer Using Various Machine Learning Strategies
Janvi Umesh Mirchandani, Siddhaling Urolagin · 2021 IEEE 6th International Conference on Computing, Communication and Automation (ICCCA) · 2021
Identification of Breast Cancer cells as Benign or Malignant is an important task for the early stages of Cancer to reduce fatality caused by it. Using Machine learning, we can predict the type of cancer, which allows us to provide better treatment. To classify the tumors, we use 7 different models such as Logistic Regression, Decision Tree Classification, K-Nearest Neighbors, Stochastic Gradient Descent, Support Vector Machine, Gaussian Naive Bayes and Random Forest Classification. These models are then compared on various evaluation metrics such as Accuracy, Precision, Recall and F1-Score to choose the best model for the prediction. This way the data is analyzed and misdiagnosis in the future can be prevented.