Salahudeen Toheeb, Maalouf Maher, Elfadel Ibrahim Abe M, Jelinek Herbert F
Department of Management Science and Engineering, Khalifa University, Abu Dhabi, UAE.
Department of Computer and Communication Engineering, Khalifa University, Abu Dhabi, UAE.
PLoS One. 2025 May 14;20(5):e0320955. doi: 10.1371/journal.pone.0320955. eCollection 2025.
Depression presents a significant challenge to global mental health, often intertwined with factors including oxidative stress. Although the precise relationship with mitochondrial pathways remains elusive, recent advances in machine learning present an avenue for further investigation. This study employed advanced machine learning techniques to classify major depressive disorders based on clinical indicators and mitochondrial oxidative stress markers. Six machine learning algorithms, including Random Forest, were applied and their performance was investigated in balanced and unbalanced data sets with respect to binary and multiclass classification scenarios. Results indicate promising accuracy and precision, particularly with Random Forest on balanced data. RF achieved an average accuracy of 92.7% and an F1 score of 83.95% for binary classification, 90.36% and 90.1%, respectively, for the classification of three classes of severity of depression and 89.76% and 88.26%, respectively, for the classification of five classes. Including only oxidative stress markers resulted in accuracy and an F1 score of 79.52% and 80.56%, respectively. Notably, including mitochondrial peptides alongside clinical factors significantly enhances predictive capability, shedding light on the interplay between depression severity and mitochondrial oxidative stress pathways. These findings underscore the potential for machine learning models to aid clinical assessment, particularly in individuals with comorbid conditions such as hypertension, diabetes mellitus, and cardiovascular disease.
抑郁症对全球心理健康构成了重大挑战，常常与包括氧化应激在内的多种因素相互交织。尽管与线粒体途径的确切关系仍不明确，但机器学习的最新进展为进一步研究提供了一条途径。本研究采用先进的机器学习技术，基于临床指标和线粒体氧化应激标志物对重度抑郁症进行分类。应用了包括随机森林在内的六种机器学习算法，并在平衡和不平衡数据集上针对二分类和多分类场景研究了它们的性能。结果显示出了有前景的准确率和精确率，尤其是在平衡数据集上使用随机森林算法时。对于二分类，随机森林算法的平均准确率达到92.7%，F1分数为83.95%；对于抑郁症严重程度的三类分类，分别为90.36%和90.1%；对于五类分类，分别为89.76%和88.26%。仅纳入氧化应激标志物时，准确率和F1分数分别为79.52%和80.56%。值得注意的是，将线粒体肽与临床因素一起纳入可显著提高预测能力，揭示了抑郁症严重程度与线粒体氧化应激途径之间的相互作用。这些发现强调了机器学习模型辅助临床评估的潜力，特别是在患有高血压、糖尿病和心血管疾病等合并症的个体中。
