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

arXiv: https://arxiv.org/abs/2404.04284 · HTML: https://mlsanity.com/paper/2404.04284/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 62 references that are in the map.
- First posted on arXiv 3 Apr 2024.

## Builds on (most cited first, 2 of 2)

- [Utilizing Neural Networks and Linguistic Metadata for Early Detection of Depression Indications in Text Sequences](https://mlsanity.com/paper/1804.07000/) (2018)
- [DEPA: Self-Supervised Audio Embedding for Depression Detection](https://mlsanity.com/paper/1910.13028/) (2019)

## Built on by (most cited first, 4 of 4)

- [Leveraging Audio and Text Modalities in Mental Health: A Study of LLMs Performance](https://mlsanity.com/paper/2412.10417/) (2024)
- [Leveraging Embedding Techniques in Multimodal Machine Learning for Mental Illness Assessment](https://mlsanity.com/paper/2504.01767/) (2025)
- [Mining Mental Health Signals: A Comparative Study of Four Machine Learning Methods for Depression Detection from Social Media Posts in Sorani Kurdish](https://mlsanity.com/paper/2508.15829/) (2025)
- [MentalMARBERT: Domain-Adaptive Pre-training and Two-Stage Fine-Tuning for Arabic Mental Health Disorders Detection](https://mlsanity.com/paper/2606.12649/) (2026)

## Abstract (arXiv)

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.