Data Recognition, Monitoring and Early Warning of Mental Health Signs based on Artificial Intelligence
Deng Chen · 2025
At present, there are common key problems in the field of mental health monitoring, such as insufficient recognition accuracy and delayed warning. This study first collected multi-dimensional mental health sign data through multiple channels. Subsequently, the collected raw data was deeply cleaned, standardized and feature extracted. The CNN-RNN model automatically learns and integrates data features from different sources. In the model training stage, this study adopted strategies such as cross-validation and early stopping to effectively prevent overfitting, and fine-tuned the model parameters through optimization methods such as grid search to further improve the prediction performance. This study also designed an efficient real-time monitoring and early warning mechanism to continuously monitor the individual’s mental health sign data. The recognition accuracy of this method can reach up to 98.5% and the minimum is 90.2%; the warning accuracy can reach up to 100% and the minimum is 95.4%, which has significant advantages in improving the efficiency and accuracy of mental health monitoring.