提出AFE算法提升医疗特征选择，让智能诊断更准更透明
Easydiagnos: a framework for accurate feature selection for automatic diagnosis in smart healthcare
- 融合遗传算法与可解释AI的自适应特征筛选方法
- 在三组医疗数据上最高达98.5%准确率，优于传统方法
- 适合需要高可靠性和可解释性的临床智能诊断场景
人工智能快速发展推动了可穿戴设备、持续监测系统和智能诊断的进步。然而，安全、可解释性、鲁棒性和性能优化仍是临床应用的主要障碍。本文提出一种基于自适应特征评估器（AFE）的创新算法，结合遗传算法（GA）、可解释人工智能（XAI）与置换组合技术（PCT），优化临床决策支持系统（CDSS），提升预测准确率与可解释性。该方法在三个不同医疗数据集上，使用六种机器学习算法验证，表现稳健且优于传统特征选择技术。结果表明，AFE具有变革性潜力，支持个性化、透明化的患者护理。尤其当与多层感知机（MLP）结合时，准确率最高达98.5%，显著提升真实医疗场景下的临床决策能力。
原文摘要 · Abstract (English)
The rapid advancements in artificial intelligence (AI) have revolutionized smart healthcare, driving innovations in wearable technologies, continuous monitoring devices, and intelligent diagnostic systems. However, security, explainability, robustness, and performance optimization challenges remain critical barriers to widespread adoption in clinical environments. This research presents an innovative algorithmic method using the Adaptive Feature Evaluator (AFE) algorithm to improve feature selection in healthcare datasets and overcome problems. AFE integrating Genetic Algorithms (GA), Explainable Artificial Intelligence (XAI), and Permutation Combination Techniques (PCT), the algorithm optimizes Clinical Decision Support Systems (CDSS), thereby enhancing predictive accuracy and interpretability. The proposed method is validated across three diverse healthcare datasets using six distinct machine learning algorithms, demonstrating its robustness and superiority over conventional feature selection techniques. The results underscore the transformative potential of AFE in smart healthcare, enabling personalized and transparent patient care. Notably, the AFE algorithm, when combined with a Multi-layer Perceptron (MLP), achieved an accuracy of up to 98.5%, highlighting its capability to improve clinical decision-making processes in real-world healthcare applications.
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