Abstract
Breast cancer prediction is crucial in identifying women who may be at risk for developing the disease. By doing the prediction, doctors can make the rapid diagnosis. Additionally, breast cancer prediction can also help guide research efforts and inform public health policies aimed at reducing the incidence and mortality of breast cancer. SVM (Support Vector Machine)is a classic method in machine learning, Random Forest is also widely used but they all have some shortcomings. Random Forest dont have high accuracy. So RF-SVM(Random Forest and Random Forest) is be chosen to do the prediction. The goal of this research is to train a model that can achieve high accuracy in a relatively short time. As for the result, it shows that RF-SVM has achieved a high accuracy(0.95), compared with other method although RF(Random Forest) has the highest accuracy(0.97), it has the lowest precision(0.95). Over all, RF-SVM has the best overall performance. After trial, traditional machine learning methods turns out to be more stable.
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