Accurate classification of depression through optimized machine learning models on high-dimensional noisy data
Accurate classification of depression through optimized machine learning models on high-dimensional noisy data
复制标题
DOI：
10.1016/j.bspc.2021.103237
复制
发表时间：
2022-01
期刊：
影响因子：
--
通讯作者：
Xingang Fang;Julia Klawohn;Alexander De Sabatino;Harsh Kundnani;Jon Ryan;Weikuan Yu;G. Hajcak
中科院分区：
文献类型：
--
作者：
Xingang Fang;Julia Klawohn;Alexander De Sabatino;Harsh Kundnani;Jon Ryan;Weikuan Yu;G. Hajcak
动机抑郁症是一种非常普遍的精神疾病，具有神经认知异常，包括减少事件相关电位（ERP）测量奖励处理和情绪反应。基于ERP数据的重性抑郁障碍（MDD）的准确分类有助于提高我们对这些改变的理解，并推动新的诊断或筛查措施。然而，由于缺乏对小样本量的噪声原始数据的泛化，它特别具有挑战性。我们的目标是使用机器学习（ML）技术，使用有噪声的ERP数据集来提高MDD的分类性能。结果我们在基于ML的ERP数据集分析中进行了两项优化：在高维噪声数据的预处理中有效提取特征，以及通过集成ML模型增强分类。结合精心设计的验证策略，我们的技术为MDD分类提供了一种高度准确的方法，即使对于样本量有限、固有噪声和高维性质的ERP数据也是如此。我们的实验结果表明，与之前执行基于回归的分类的研究相比，我们的ML优化同时实现了很高的准确性和近乎完美的灵敏度，特别是在对训练过程中看不到的数据样本进行分类时。补充信息提供ERP数据收集的补充文件。
MotivationDepressive disorders are highly prevalent and impairing psychiatric conditions with neurocognitive abnormalities, including reduced event-related potential (ERP) measures of reward processing and emotional reactivity. Accurate classification of Major Depressive Disorder (MDD) based on ERP data could help improve our understanding of these alterations and propel novel diagnostic or screening measures. However, it has been particularly challenging due to the lack of generalization for noisy raw data with small sample sizes. We aim to improve classification performance for MDD using noisy ERP datasets using machine learning (ML) techniques.ResultsWe have developed two optimizations in our ML-based analysis of ERP datasets: effective feature extraction in the preprocessing of high-dimensional noisy data and enhanced classification through ensemble ML models. Together with a carefully designed validation strategy, our techniques provide a highly accurate method for MDD classification even for ERP data that are limited in sample size, inherently noisy and high-dimensional in nature. Our experimental results demonstrate that our ML optimizations achieve great accuracy and nearly perfect sensitivity simultaneously, particularly in classifying data samples unseen during the training process, compared to prior studies that perform regression-based classifications.Supplementary informationA supplementary document on ERP data collection is available.