Assessing ML Classification Algorithms and NLP Techniques for Depression Detection: An Experimental Case Study

arXiv:2404.04284v1 Announce Type: new
Abstract: 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 …

abstract algorithms arxiv become case case study classification costs cs.cl depression detection experimental health major nlp nlp techniques people public public health reduce shortage study type