Summary of Easydiagnos: a Framework For Accurate Feature Selection For Automatic Diagnosis in Smart Healthcare, by Prasenjit Maji et al.
Easydiagnos: a framework for accurate feature selection for automatic diagnosis in smart healthcare
by Prasenjit Maji, Amit Kumar Mondal, Hemanta Kumar Mondal, Saraju P. Mohanty
First submitted to arxiv on: 1 Oct 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 research presents an innovative algorithmic method using the Adaptive Feature Evaluator (AFE) algorithm to overcome security, explainability, robustness, and performance optimization challenges in smart healthcare. The AFE algorithm integrates Genetic Algorithms (GA), Explainable Artificial Intelligence (XAI), and Permutation Combination Techniques (PCT) to optimize Clinical Decision Support Systems (CDSS), enhancing predictive accuracy and interpretability. The method is validated across three diverse healthcare datasets using six distinct machine learning algorithms, demonstrating its robustness and superiority over conventional feature selection techniques. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper introduces a new algorithm called AFE that helps make better decisions in healthcare by choosing the most important features from large amounts of data. It’s like a super-smart filter that picks out what’s most useful. The researchers tested it on three different types of healthcare data and showed that it works really well, even when compared to other methods. This could be very helpful for doctors and patients because it can help them make more accurate predictions about people’s health. |
Keywords
» Artificial intelligence » Feature selection » Machine learning » Optimization
