Assessing ML Classification Algorithms and NLP Techniques for Depression
  Detection: An Experimental Case Study
Giuliano Lorenzoni Cristina Tavares Nathalia Nascimento Paulo Alencar Donald Cowan
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.