  The prediction of disease risk factors can screen vulnerable groups for
effective prevention and treatment, so as to reduce their morbidity and
mortality. Machine learning has a great demand for high-quality labeling
information, and labeling noise in medical big data poses a great challenge to
efficient disease risk warning methods. Therefore, this project intends to
study the robust learning algorithm and apply it to the early warning of
infectious disease risk. A dynamic truncated loss model is proposed, which
combines the traditional mutual entropy implicit weight feature with the mean
variation feature. It is robust to label noise. A lower bound on training loss
is constructed, and a method based on sampling rate is proposed to reduce the
gradient of suspected samples to reduce the influence of noise on training
results. The effectiveness of this method under different types of noise was
verified by using a stroke screening data set as an example. This method
enables robust learning of data containing label noise.
