Easydiagnos: a framework for accurate feature selection for automatic diagnosis in smart healthcare

Feature selection directly impacts diagnostic accuracy: The research demonstrates that carefully choosing which data points (features) to use in AI diagnostic systems significantly improves performance, with the proposed AFE method achieving up to 98.5% accuracy when combined with machine learning models—suggesting that smarter data selection, not just more data, drives better healthcare outcomes.

Explainability is essential for clinical adoption: By integrating Explainable AI (XAI) into their framework, the researchers address a critical barrier to real-world healthcare use—doctors and patients need to understand why an AI system makes a diagnosis recommendation, not just accept its predictions as a "black box."

Robustness across different datasets matters for real-world deployment: The validation across three diverse healthcare datasets indicates the method works reliably in varied clinical contexts, which is crucial for responsible AI implementation since healthcare systems must perform consistently across different patient populations and medical conditions.

Optimization of existing systems can reduce barriers to adoption: Rather than requiring entirely new infrastructure, the AFE algorithm improves current Clinical Decision Support Systems by enhancing both accuracy and interpretability, suggesting a practical pathway for healthcare institutions to responsibly integrate advanced AI without complete system overhauls.