An AI-Based System for Early Detection of Mental Health Crisis

Abstract

Mental health diseases have become a critical global concern, with early detection playing a vital part in prevention and timely intervention. Traditional assessment methods rely heavily on self-reporting and clinical interviews, which are often delayed or inaccessible. This paper proposes MindBloom, an artificial intelligence–based system that leverages Natural Language Processing (NLP) and Machine Learning (ML) techniques to detect early signals of mental health crises from textual user inputs. The system preprocesses user-generated text, extracts linguistic and emotional features, and applies supervised machine learning models for classification. Experimental results demonstrate that the proposed approach achieves promising accuracy in identifying potential mental health risk levels. The findings suggest that MindBloom can serve as an effective decision-support tool for early mental health screening while maintaining scalability and ethical considerations.

Introduction

Mental health conditions like depression, anxiety, and emotional distress are often detected late, increasing risks such as self-harm and suicide. With the rise of digital communication, people increasingly express emotions through text, enabling AI-based analysis using Natural Language Processing (NLP) and Machine Learning (ML). This paper introduces MindBloom, an AI system that analyzes user text to detect and classify mental health risk levels for early intervention support.

MindBloom follows a structured pipeline: data collection from anonymized and public datasets, text preprocessing (tokenization, cleaning, stemming, lemmatization), feature extraction (TF-IDF, sentiment scores, and emotion indicators), and classification using ML models such as Naïve Bayes, Logistic Regression, SVM, and Random Forest. The system categorizes text into low, moderate, or high risk levels. Among the tested models, Support Vector Machine (SVM) performed best in accuracy and overall evaluation metrics.

The system architecture includes a user interface, preprocessing module, feature extraction engine, and classification layer. Results show that combining NLP-based features with ML classifiers is effective for identifying early mental health warning signs. The system is also designed to be interpretable, ethically safe (using anonymized data), and scalable for integration into healthcare platforms via APIs.

Overall, MindBloom is a decision-support tool aimed at early mental health risk detection using interpretable AI methods, not as a replacement for clinical diagnosis.

Conclusion

This paper presented MindBloom, an AI-based system for early detection of mental health crises using NLP and ML techniques. The results validate the feasibility of automated screening through textual analysis. Future work includes integrating deep learning models, expanding multilingual support, and incorporating real-time behavioral data while ensuring ethical and privacy standards.

References

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Copyright

Copyright © 2026 Prof. Rajnandini B. Kumawat, Mr. Rahul S. Mourya, Mr. Rajat Y. Dubey, Mr. Harsh D. Agre, Mr. Yash B. Chaubey. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.