基于心内膜心肌活检标本的心肌病诊断模型：小样本数据下的合适特征空间与类别边界
Cardiomyopathy Diagnosis Model from Endomyocardial Biopsy Specimens: Appropriate Feature Space and Class Boundary in Small Sample Size Data
AI总结：
针对心肌病病理诊断小样本数据问题，研究验证纹理特征有效性，发现特征数样本量比高时，结合特征选择与维度压缩的多步流程可提升模型泛化性能，线性核SVM效果最优，为医疗场景心肌病诊断模型开发提供支持。
摘要：
随着心力衰竭患者数量增加，由于病理学家短缺，机器学习（ML）在心肌病诊断领域受到关注。然而，心内膜心肌活检标本通常为小样本数据，需要特征提取、降维等技术。本研究旨在确定纹理特征在心肌病病理诊断的特征提取中是否有效。此外，研究通过对多个机器学习模型应用特征选择（FS）和维度压缩（DC），探究了有助于提升泛化性能的模型设计。研究通过可视化类间分布差异、基于纹理特征开展统计假设检验，对所得结果进行了验证；同时还通过不同模型设计（特征选择与维度压缩的应用与否组合不同、决策边界不同）下的预测性能对结果进行了评估。研究结果证实，纹理特征对心肌病病理诊断可能有效。此外，当特征数与样本量的比值较高时，包含特征选择和维度压缩的多步骤流程可提升泛化性能，其中线性核支持向量机取得了最优结果。研究证明，无论决策边界是线性、曲线形、与坐标轴垂直还是平行，该流程对复杂度降低的模型都可能有效。这些发现有望推动有效心肌病诊断模型的开发，以快速应用于医疗实践。
英文摘要：
As the number of patients with heart failure increases, machine learning (ML) has garnered attention in cardiomyopathy diagnosis, driven by the shortage of pathologists. However, endomyocardial biopsy specimens are often small sample size and require techniques such as feature extraction and dimensionality reduction. This study aims to determine whether texture features are effective for feature extraction in the pathological diagnosis of cardiomyopathy. Furthermore, model designs that contribute toward improving generalization performance are examined by applying feature selection (FS) and dimensional compression (DC) to several ML models. The obtained results were verified by visualizing the inter-class distribution differences and conducting statistical hypothesis testing based on texture features. Additionally, they were evaluated using predictive performance across different model designs with varying combinations of FS and DC (applied or not) and decision boundaries. The obtained results confirmed that texture features may be effective for the pathological diagnosis of cardiomyopathy. Moreover, when the ratio of features to the sample size is high, a multi-step process involving FS and DC improved the generalization performance, with the linear kernel support vector machine achieving the best results. This process was demonstrated to be potentially effective for models with reduced complexity, regardless of whether the decision boundaries were linear, curved, perpendicular, or parallel to the axes. These findings are expected to facilitate the development of an effective cardiomyopathy diagnostic model for its rapid adoption in medical practice.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
