Depression has affected millions of people worldwide and has become one of
the most common mental disorders. Early mental disorder detection can reduce
costs for public health agencies and prevent other major comorbidities.
Additionally, the shortage of specialized personnel is very concerning since
Depression diagnosis is highly dependent on expert professionals and is
time-consuming. Recent research has evidenced that machine learning (ML) and
Natural Language Processing (NLP) tools and techniques have significantly bene
ted the diagnosis of depression. However, there are still several challenges in
the assessment of depression detection approaches in which other conditions
such as post-traumatic stress disorder (PTSD) are present. These challenges
include assessing alternatives in terms of data cleaning and pre-processing
techniques, feature selection, and appropriate ML classification algorithms.
This paper tackels such an assessment based on a case study that compares
different ML classifiers, specifically in terms of data cleaning and
pre-processing, feature selection, parameter setting, and model choices. The
case study is based on the Distress Analysis Interview Corpus - Wizard-of-Oz
(DAIC-WOZ) dataset, which is designed to support the diagnosis of mental
disorders such as depression, anxiety, and PTSD. Besides the assessment of
alternative techniques, we were able to build models with accuracy levels
around 84% with Random Forest and XGBoost models, which is significantly higher
than the results from the comparable literature which presented the level of
accuracy of 72% from the SVM model.

此研究评估了使用机器学习和自然语言处理技术来检测抑郁症的方法，针对数据清理、模型选择等方面进行了案例研究，并在具体实验中使用 Distress Analysis Interview Corpus - Wizard-of-Oz（DAIC-WOZ）数据集构建出准确率约为 84% 的模型，明显高于现有文献中通过 SVM 模型得到的 72% 的准确率。