Summary of Research on Disease Prediction Model Construction Based on Computer Ai Deep Learning Technology, by Yang Lin et al.
Research on Disease Prediction Model Construction Based on Computer AI deep Learning Technology
by Yang Lin, Muqing Li, Ziyi Zhu, Yinqiu Feng, Lingxi Xiao, Zexi Chen
First submitted to arxiv on: 23 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed dynamic truncated loss model is designed to predict disease risk factors more accurately, even when dealing with noisy labeling information common in medical big data. By combining traditional mutual entropy implicit weight features with mean variation features, the model can learn robustly and effectively screen vulnerable groups for early prevention and treatment. A lower bound on training loss is established, and a sampling rate-based method reduces the gradient of suspected samples to minimize the impact of noise. The approach is tested on a stroke screening dataset, demonstrating its effectiveness under various types of label noise. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research aims to develop a machine learning algorithm that can accurately predict disease risk factors despite noisy labeling information in medical data. By creating a robust model, we can identify vulnerable groups early and help prevent and treat diseases more effectively. The proposed approach uses a combination of features to reduce the impact of noisy labels and establish a lower bound on training loss. |
Keywords
» Artificial intelligence » Machine learning
