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Detecting depression using an ensemble classifier based on Quality of Life scales
Brain Informatics Pub Date : 2021-02-15 , DOI: 10.1186/s40708-021-00125-5
Xiaohui Tao , Oliver Chi , Patrick J. Delaney , Lin Li , Jiajin Huang

Major depressive disorder (MDD) is an issue that affects 350 million people worldwide. Traditional approaches have been to identify depressive symptoms in datasets, but recently, research is beginning to explore the association between psychosocial factors such as those on the quality of life scale and mental well-being, which will lead to earlier diagnosis and prediction of MDD. In this research, an ensemble binary classifier is proposed to analyse health survey data against ground truth from the SF-20 Quality of Life scales. The classifier aims to improve the performance of machine learning techniques on large datasets and identify depressed cases based on associations between items on the QoL scale and mental illness by increasing predictive performance. On the experimental evaluation on the National Health and Nutrition Examination Survey (NHANES), the classifier demonstrated an F1 score of 0.976 in the prediction, without any incorrectly identified depression instances. Only about 4% of instances had been mistakenly classified into depressed cases, with a significant accuracy of 95.4% comparing to the result from PHQ-9 mental screen inventory. The presented ensemble binary classifier performed comparably better than each baseline algorithm in all measures and all experiments. We trained the ensemble model on the processed NHANES dataset, tested and evaluated the results of its performance against mental screen inventory and discussed the comparable predictions. Finally, we provided future research directions.

中文翻译:

使用基于生活质量量表的整体分类器检测抑郁

严重抑郁症(MDD)是一个影响全球3.5亿人的问题。传统的方法一直是识别数据集中的抑郁症状,但是最近,研究开始探索心理社会因素之间的关联,例如生活质量量表和心理健康方面的因素,这将导致MDD的早期诊断和预测。在这项研究中,提出了整体二元分类器,以根据SF-20生活质量量表的地面真实性分析健康调查数据。该分类器旨在提高大型数据集上机器学习技术的性能,并通过提高预测性能,根据QoL量表上的项目与精神疾病之间的关联来确定抑郁症患者。根据国家健康和营养检查调查(NHANES)的实验评估,分类器在预测中显示F1评分为0.976,没有任何错误地识别出抑郁症的情况。仅约4%的实例被错误地分类为抑郁症病例,与PHQ-9精神筛查清单的结果相比,其准确率为95.4%。所提出的集成二进制分类器在所有测量和所有实验中的性能均优于每种基线算法。我们在处理后的NHANES数据集上训练了集成模型,测试并评估了针对心理筛查清单的性能结果,并讨论了可比较的预测。最后,我们提供了未来的研究方向。没有发现任何不正确的抑郁症实例。仅约4%的实例被错误地分类为抑郁症病例,与PHQ-9精神筛查清单的结果相比,其准确率为95.4%。所提出的集成二进制分类器在所有测量和所有实验中的性能均优于每种基线算法。我们在处理后的NHANES数据集上训练了集成模型,测试并评估了针对心理筛查清单的性能结果,并讨论了可比较的预测。最后,我们提供了未来的研究方向。没有发现任何不正确的抑郁症实例。仅约4%的实例被错误地分类为抑郁症病例,与PHQ-9精神筛查清单的结果相比,其准确率为95.4%。所提出的集成二进制分类器在所有测量和所有实验中的性能均优于每种基线算法。我们在处理后的NHANES数据集上训练了集成模型,测试并评估了针对心理筛查清单的性能结果,并讨论了可比较的预测。最后,我们提供了未来的研究方向。所提出的集成二进制分类器在所有测量和所有实验中的性能均优于每种基线算法。我们在处理后的NHANES数据集上训练了集成模型,测试并评估了针对心理筛查清单的性能结果,并讨论了可比较的预测。最后,我们提供了未来的研究方向。所提出的集成二进制分类器在所有测量和所有实验中的性能均优于每种基线算法。我们在处理后的NHANES数据集上训练了集成模型,测试并评估了针对心理筛查清单的性能结果,并讨论了可比较的预测。最后,我们提供了未来的研究方向。
更新日期:2021-02-16
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