Research on Disease Prediction Model Construction Based on Computer AI deep Learning Technology
This work addresses disease risk prediction for vulnerable groups, but it appears incremental as it builds on existing robust learning methods for label noise.
The study tackled the challenge of label noise in medical big data for disease risk prediction by proposing a dynamic truncated loss model, which was validated on a stroke screening dataset to enable robust learning under different noise types.
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.