【摘要】 随着人工智能在医疗领域的融合，基于影像学仪器与可穿戴设备的智能辅助系统不断发展，推动疾病早期诊断成为可能。脑小血管病（Cerebral Small Vessel Disease, CSVD）作为常见老年性脑血管疾病，起病隐匿、进展缓慢，严重影响生活质量。然而，其数据获取困难且类别不平衡，为智能预测模型构建带来挑战。文中基于机器学习技术，围绕脑小血管病智能预测开展研究。首先，进行了关键危险因素筛选；然后，采用SMOTE-Tomek Links方法对不平衡数据进行处理；最后，进行了脑小血管病精准危险预测模型构建，并通过五折交叉验证对模型的性能进行了验证。实验结果表明：所提方法在小样本不平衡环境下，能够显著提升准确率、召回率和接受者操作特征曲线下面积（Area Under Curve, AUC）值，为脑小血管病的早期预测与诊断提供了一种有效方案。更多还原

【Abstract】 With the integration of artificial intelligence into the medical field, intelligent auxiliary systems based on imaging devices and wearable technologies have been rapidly evolving, facilitating the early diagnosis of diseases. As a common cerebrovascular disease in the elderly, cerebral small vessel disease(CSVD) has an insidious onset and slow progression, significantly impairing patients’ quality of life. However, challenges such as limited data availability and class imbalance hinder the development of effective predictive models. Machine learning techniques are employed in this paper to investigate the intelligent prediction of CSVD. Firstly, key risk factors are identified through feature selection. Then, the SMOTE-Tomek Links method is used to address the issue of imbalanced data. Finally, a precise risk prediction model for CSVD is developed and validated using five-fold cross-validation. Experimental results demonstrate that the proposed method can significantly improve accuracy, recall, and area under curve(AUC) under conditions of imbalanced limitedsample data, providing an effective solution for the early prediction and diagnosis of CSVD.更多还原