Using Random Forest Algorithm for Breast Cancer Diagnosis

In the current health care field, benefit from the provision of medical big data, machine learning can be used to obtain knowledge from the data. Machine learning methods can describe cases from an objective perspective, and predictions of diagnostic results can be generated from a combination of related pathological factors. The introduction of machine learning for the way of medical diagnosis and the accuracy of diagnosis is a major change and inevitable direction of the future medical model. In this paper, the random forest algorithm is used to analyze the medical case diagnosis of breast cancer. The random forest algorithm can combine the characteristics of multiple eigenvalues, and the combined results of multiple decision trees can be used to improve the prediction accuracy. Based on the ensemble learning method of random forests, the results of multiple weak classifiers can be combined to produce accurate classification results. In this paper, a random forest algorithm is used to discuss the case of breast cancer case diagnosis and obtain high prediction accuracy. It has practical significance for auxiliary medical diagnosis.