Mental Health Classification Using Machine Learning and Natural Language Processing

Mental health disorders such as depression, anxiety and stress are becoming increasingly common in
modern society and significantly affect an individual’s emotional well-being, productivity, and quality of life. Early
identification of mental health conditions is essential for providing timely support and preventive care. This project
presents a Mental Health Classification System that utilizes machine learning and natural language processing
(NLP) techniques to automatically classify mental health conditions based on textual or survey data. The system
collects datasets containing user responses, social media posts, or psychological assessment records and performs
data preprocessing steps such as text cleaning, tokenization, stop-work removal, and normalization. Feature extraction
techniques including TF-IDF and word embedding’s are applied to transform textual data into numerical
representations. These features are then used to train classification models such as logistic Regression, Random
Forest, Support Vector Machine, and deep learning models like LSTM or BERT models to accurately predict mental
health categories such as healthy, mild, moderate, or severe. The system architecture consists of a frontend developed
using HTML, CSS, and JavaScript that provides a simple and interactive interface for users to enter their responses
or text inputs, while the backend is implemented using Python with frameworks such as Flask or Django to handle
data processing, model training. and prediction generation. The trained model analyzes the input data and returns
instant classification results along with insights into potential mental health risk factors. By integrating machine
learning with a web-based interface, the system aims to support early mental health monitoring, improve awareness,
and assist healthcare professionals in decision-making for preventive and personalized mental healthcare