Ku Wai Lim, Min Hua
Systems Biology Center, National Heart, Lung and Blood Institute, NIH, Bethesda, MD 20892, USA.
Department of Health Administration and Policy, College of Public Health, George Mason University, Fairfax, VA 22030, USA.
Healthcare (Basel). 2024 Mar 10;12(6):0. doi: 10.3390/healthcare12060625.
Major Depressive Disorder (MDD) and Generalized Anxiety Disorder (GAD) pose significant burdens on individuals and society, necessitating accurate prediction methods. Machine learning (ML) algorithms utilizing electronic health records and survey data offer promising tools for forecasting these conditions. However, potential bias and inaccuracies inherent in subjective survey responses can undermine the precision of such predictions. This research investigates the reliability of five prominent ML algorithms-a Convolutional Neural Network (CNN), Random Forest, XGBoost, Logistic Regression, and Naive Bayes-in predicting MDD and GAD. A dataset rich in biomedical, demographic, and self-reported survey information is used to assess the algorithms' performance under different levels of subjective response inaccuracies. These inaccuracies simulate scenarios with potential memory recall bias and subjective interpretations. While all algorithms demonstrate commendable accuracy with high-quality survey data, their performance diverges significantly when encountering erroneous or biased responses. Notably, the CNN exhibits superior resilience in this context, maintaining performance and even achieving enhanced accuracy, Cohen's kappa score, and positive precision for both MDD and GAD. This highlights the CNN's superior ability to handle data unreliability, making it a potentially advantageous choice for predicting mental health conditions based on self-reported data. These findings underscore the critical importance of algorithmic resilience in mental health prediction, particularly when relying on subjective data. They emphasize the need for careful algorithm selection in such contexts, with the CNN emerging as a promising candidate due to its robustness and improved performance under data uncertainties.
重度抑郁症（MDD）和广泛性焦虑症（GAD）给个人和社会带来了沉重负担，因此需要准确的预测方法。利用电子健康记录和调查数据的机器学习（ML）算法为预测这些疾病提供了有前景的工具。然而，主观调查回答中固有的潜在偏差和不准确可能会削弱此类预测的准确性。本研究调查了五种著名的ML算法——卷积神经网络（CNN）、随机森林、XGBoost、逻辑回归和朴素贝叶斯——在预测MDD和GAD方面的可靠性。一个包含丰富生物医学、人口统计学和自我报告调查信息的数据集被用于评估这些算法在不同程度主观回答不准确情况下的性能。这些不准确模拟了存在潜在记忆回忆偏差和主观解释的情景。虽然所有算法在高质量调查数据下都表现出值得称赞的准确性，但在遇到错误或有偏差的回答时，它们的性能会有显著差异。值得注意的是，在这种情况下，CNN表现出卓越的弹性，在预测MDD和GAD时保持性能，甚至提高了准确性、科恩kappa系数和阳性预测值。这凸显了CNN处理数据不可靠性的卓越能力，使其成为基于自我报告数据预测心理健康状况的一个潜在优势选择。这些发现强调了算法弹性在心理健康预测中的至关重要性，特别是在依赖主观数据时。它们强调了在这种情况下仔细选择算法的必要性，由于其稳健性和在数据不确定性下的性能提升，CNN成为一个有前景的候选算法。
