Summary of Research on Early Warning Model Of Cardiovascular Disease Based on Computer Deep Learning, by Yuxiang Hu et al.
Research on Early Warning Model of Cardiovascular Disease Based on Computer Deep Learning
by Yuxiang Hu, Jinxin Hu, Ting Xu, Bo Zhang, Jiajie Yuan, Haozhang Deng
First submitted to arxiv on: 13 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 cardiovascular disease risk early warning model leverages one-dimensional convolutional neural networks to enhance forecasting precision by 11.2% compared to conventional approaches. The model first addresses missing values in physiological and symptom indicators, followed by Z-score standardization. A 2D matrix is constructed by converting the convolutional neural network, utilizing first-order convolution operations with convolution functions of 1,3, and 5, along with Max Pooling for dimension reduction. The Adam algorithm optimizes the model’s learning rate and output rate, while a soft classifier outputs classification results. The study demonstrates the efficacy of this novel approach using Statlog in the UCI database and heart disease database. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way to predict when someone might get a serious heart condition is being tested. This method uses special kinds of math problems called neural networks to look at important signs like age, blood sugar levels, and cholesterol numbers. The goal is to make predictions more accurate than usual methods. To do this, the team first fills in missing information and then adjusts it so that all numbers are on a similar scale. They use different-sized “filters” to help identify patterns in the data and reduce the amount of information they need to consider. This approach was tested using real heart disease data and showed significant improvements over traditional methods. |
Keywords
» Artificial intelligence » Classification » Neural network » Precision
