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Using Pupil Diameter for Psychological Resilience Assessment in Medical Students Based on SVM and SHAP Model

支持向量机 随机森林 可解释性 计算机科学 人工智能 逻辑回归 机器学习
作者
Fayang Xiang,Li Zhang,Yidan Ye,Chuyue Xiong,Yanjie Zhang,Yan Hu,Jiang Du,Yi Zhou,Qiyue Deng,Xinke Li
出处
期刊:IEEE Journal of Biomedical and Health Informatics [Institute of Electrical and Electronics Engineers]
卷期号:28 (7): 4260-4268 被引量:1
标识
DOI:10.1109/jbhi.2024.3390390
摘要

Effectively assessing psychological resilience for medical students is vital for identifying at-risk individuals and developing tailored interventions. At present, few studies have combined physiological indexes of the human body and machine learning for psychological resilience assessment. This study presents a novel approach that employs pupil diameter features and machine learning to predict psychological resilience risk objectively. Firstly, we designed a stimulus paradigm (via auditory and visual stimuli) and collected pupil diameter data from participants using eye-tracking technology. Secondly, the pupil data was preprocessed, including linear interpolation, blink detection, and subtractive baseline correction. Thirdly, statistical metrics were extracted and optimal feature subsets were obtained by Recursive Feature Elimination with Cross-Validation (RFECV). Subsequently, the classification models, including Logistic Regression (LR), Random Forest (RF), Support Vector Machine (SVM), and eXtreme Gradient Boosting (XGBoost), were trained. The experimental results show that the SVM model has the best performance, and its balance accuracy, recall, and AUC reach 0.906, 0.89, and 0.932, respectively. Finally, we leveraged the Shapley additive explanation (SHAP) model for interpretability analysis. It revealed auditory stimuli have a more significant effect than visual stimuli in psychological resilience assessment. These findings suggested that pupil diameter could be a vital metric for assessing psychological resilience.
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