用行为与面部数据提升自闭症诊断公平性，机器学习模型表现亮眼
Towards Equitable ASD Diagnostics: A Comparative Study of Machine and Deep Learning Models Using Behavioral and Facial Data
- 结合行为与面部数据，用随机森林和卷积网络做诊断分析
- 随机森林达100%验证准确率，有效降低误诊，适合早期干预
- MobileNet在图像识别中87%准确率，轻量结构利于资源有限环境
自闭症谱系障碍（ASD）在女性中常被漏诊，因传统诊断忽略性别相关的症状差异。本研究评估了机器学习模型，特别是随机森林和卷积神经网络，在结构化数据与面部图像分析中的应用效果。随机森林在多个数据集上实现100%验证准确率，展现出处理复杂关系的能力并减少假阴性，对早期干预及缓解性别偏见至关重要。在图像分析中，MobileNet超越基线CNN，达到87%准确率，但30%验证损失表明存在过拟合风险，需进一步优化以增强临床鲁棒性。未来工作将聚焦超参数调优、正则化与迁移学习。融合行为数据与面部分析有望改善对易漏诊群体的诊断。研究结果表明，随机森林在高精度与精准率-召回率平衡方面表现优异，可增强临床流程；MobileNet轻量化架构也显示在资源受限环境下开展可及性筛查的潜力。模型可解释性与临床信任度建设将是关键挑战。
原文摘要 · Abstract (English)
Autism Spectrum Disorder (ASD) is often underdiagnosed in females due to gender-specific symptom differences overlooked by conventional diagnostics. This study evaluates machine learning models, particularly Random Forest and convolutional neural networks, for enhancing ASD diagnosis through structured data and facial image analysis. Random Forest achieved 100% validation accuracy across datasets, highlighting its ability to manage complex relationships and reduce false negatives, which is crucial for early intervention and addressing gender biases. In image-based analysis, MobileNet outperformed the baseline CNN, achieving 87% accuracy, though a 30% validation loss suggests possible overfitting, requiring further optimization for robustness in clinical settings. Future work will emphasize hyperparameter tuning, regularization, and transfer learning. Integrating behavioral data with facial analysis could improve diagnosis for underdiagnosed groups. These findings suggest Random Forest's high accuracy and balanced precision-recall metrics could enhance clinical workflows. MobileNet's lightweight structure also shows promise for resource-limited environments, enabling accessible ASD screening. Addressing model explainability and clinician trust will be vital.
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