This project develops a Depression Detection System using Machine Learning on Twitter data. It predicts depression by analyzing tweets with SVM, Logistic Regression, Decision Trees, and NLTK in Python.

Web app para auto-aplicação da escala PHQ-9 para depressão.

Analyzed time-series data (Depressjon) to detect depression from patient activity recorded via clinical actigraphy watches. Utilized features such as time domain, statistical metrics, and LSTM-extracted attributes.

This is an academic final year individual project, published by Liew Jun Yen - Data Analyst

Harnessing large language models over transformer models for detecting Bengali depressive social media text: A comprehensive study

Depression detection with federated learning

Official source code for the paper: "Reading Between the Frames Multi-Modal Non-Verbal Depression Detection in Videos"

Prompt: والحرب تجعل كل شيءٍ واضحٍ .... 🖊 Output: No depression e` Propability of 99.9% 🧠⏳

Depression detection using machine learning is a vital area of research given the global burden of mental health disorders. This project explores two primary methodologies: leveraging depression quiz tests and analyzing sentences.

A trained machine learning model to detect early symptoms of depression using data collected from X (Twitter) that is integrated into Telegram.

This Project creates a smart AI computer program that predicts depression from what people write online. It learns patterns in language to spot signs of depression, helping identify those who might need support.

Twitter Depression Detection

OpticalDR: A Deep Optical Imaging Model for Privacy-Protective Depression Recognition

code for paper 'Spatial-Temporal Attention Network for Depression Recognition from Facial Videos'

Mentoso : Mental health detection and Counselling application

This is a repo for a side project about detecting depression using ML

This is an implementation of the attention-based hybrid architecture (Ghosh et al, 2023) for suicide/depressive social media notes detection.

Official source code for the paper: "It’s Just a Matter of Time: Detecting Depression with Time-Enriched Multimodal Transformers"

A mental health quiz app to help individuals check in with themselves.