Research on Disease Prediction Model Construction Based on Computer AI deep Learning Technology

TL;DR

This paper proposes a robust deep learning-based disease prediction model that effectively handles noisy labels in medical data, improving early infectious disease risk warning accuracy.

Contribution

It introduces a dynamic truncated loss model combining mutual entropy and mean variation features to enhance robustness against label noise in disease prediction.

Findings

Effective noise resistance demonstrated on stroke screening data

Reduces influence of noisy labels during training

Improves disease risk prediction accuracy

Abstract

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.