Summary of Predicting Coronary Heart Disease Using a Suite Of Machine Learning Models, by Jamal Al-karaki et al.
Predicting Coronary Heart Disease Using a Suite of Machine Learning Models
by Jamal Al-Karaki, Philip Ilono, Sanchit Baweja, Jalal Naghiyev, Raja Singh Yadav, Muhammad Al-Zafar Khan
First submitted to arxiv on: 21 Sep 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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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 A novel machine learning approach is proposed to diagnose and predict coronary heart disease (CHD), a prevalent healthcare issue affecting millions worldwide. By leveraging supervised learning algorithms, researchers aim to provide a low-cost, non-invasive solution for early diagnosis. The study compares the performance of several well-known methods, including Random Forest with oversampling, achieving an accuracy rate of 84%. This research contributes to the development of more accurate and efficient diagnostic tools for CHD. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way is being explored to detect heart disease earlier and more accurately. Right now, there are many methods that can diagnose heart disease, but they have some problems like being invasive or not detecting it soon enough. A team of researchers used special computer programs called machine learning algorithms to find a solution that’s low-cost and non-invasive. They tested different approaches and found that one method, called Random Forest with oversampling, worked best, achieving an accuracy rate of 84%. This study is important because it could help people get diagnosed earlier and receive better treatment. |
Keywords
» Artificial intelligence » Machine learning » Random forest » Supervised
