用可解释深度学习模型辅助心理医生更准确地诊断注意力缺陷多动障碍。
Enhancing Psychologists' Understanding through Explainable Deep Learning Framework for ADHD Diagnosis
- 融合微调的DNN与RNN，构建可解释的混合模型用于ADHD检测。
- 二分类F1达99%，多分类准确率94.2%，显著提升诊断性能。
- 结合SHAP和特征重要性分析，帮助心理医生理解模型决策逻辑。
注意缺陷多动障碍（ADHD）是一种神经发育障碍，诊断困难，需依赖先进且透明的方法进行可靠识别与分类。其特征为注意力不集中、多动与冲动行为，程度远超同龄人。本文提出一种基于微调混合深度神经网络（DNN）与循环神经网络（RNN）的可解释框架——HyExDNN-RNN，用于ADHD检测、多类别分类及决策解释。该框架不仅实现精准诊断，还提供可解释性洞察，增强心理学家对结果的信任。采用皮尔逊相关系数进行最优特征选择，并通过标准化技术完成特征降维、模型选型与解释。实验表明，该框架在二分类任务中达到99% F1分数，在多分类任务中达94.2%准确率。XAI方法（如SHAP与置换特征重要性）揭示了关键特征贡献与模型决策逻辑。通过融合人工智能与临床经验，本研究旨在弥合先进计算技术与实际心理应用之间的差距，展现该框架在辅助ADHD诊断与解读方面的潜力。
原文摘要 · Abstract (English)
Attention Deficit Hyperactivity Disorder (ADHD) is a neurodevelopmental disorder that is challenging to diagnose and requires advanced approaches for reliable and transparent identification and classification. It is characterized by a pattern of inattention, hyperactivity and impulsivity that is more severe and more frequent than in individuals with a comparable level of development. In this paper, an explainable framework based on a fine-tuned hybrid Deep Neural Network (DNN) and Recurrent Neural Network (RNN) called HyExDNN-RNN model is proposed for ADHD detection, multi-class categorization, and decision interpretation. This framework not only detects ADHD, but also provides interpretable insights into the diagnostic process so that psychologists can better understand and trust the results of the diagnosis. We use the Pearson correlation coefficient for optimal feature selection and machine and deep learning models for experimental analysis and comparison. We use a standardized technique for feature reduction, model selection and interpretation to accurately determine the diagnosis rate and ensure the interpretability of the proposed framework. Our framework provided excellent results on binary classification, with HyExDNN-RNN achieving an F1 score of 99% and 94.2% on multi-class categorization. XAI approaches, in particular SHapley Additive exPlanations (SHAP) and Permutation Feature Importance (PFI), provided important insights into the importance of features and the decision logic of models. By combining AI with human expertise, we aim to bridge the gap between advanced computational techniques and practical psychological applications. These results demonstrate the potential of our framework to assist in ADHD diagnosis and interpretation.
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