Summary of Early Detection Of Coronary Heart Disease Using Hybrid Quantum Machine Learning Approach, by Mehroush Banday et al.
Early Detection of Coronary Heart Disease Using Hybrid Quantum Machine Learning Approach
by Mehroush Banday, Sherin Zafar, Parul Agarwal, M Afshar Alam, Abubeker K M
First submitted to arxiv on: 17 Sep 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 paper presents a novel approach for early diagnosis of coronary heart disease (CHD) using quantum machine learning (QML). By combining QML classifiers with classical machine learning algorithms, the authors propose a hybrid ensemble model that can efficiently process multidimensional healthcare data. This approach has been tested on a broad dataset integrating clinical and imaging data from patients with CHD and healthy controls, showing improved accuracy, sensitivity, F1 score, and specificity compared to classical machine learning models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper uses quantum computing to improve the diagnosis of coronary heart disease. The authors create a special machine learning model that combines different techniques and can look at lots of health data at once. This helps doctors find out if someone has heart disease earlier and more accurately than before. This is important because early detection can help people get better treatment and avoid serious problems. |
Keywords
» Artificial intelligence » Ensemble model » F1 score » Machine learning
