Design and application of a psychological prediction system for depression based on artificial intelligence algorithms

With the accelerated pace of society and the continuous accumulation of psychological stress, depression has become a mental health problem of widespread concern worldwide. Traditional means of depression screening and prediction rely on manual interviews and questionnaires, which have limitations such as high subjectivity and low efficiency. In order to enhance the intelligence of psychological prediction, this paper designs and implements a depression psychological prediction system based on artificial intelligence algorithms. The system integrates multi-source data acquisition, feature engineering construction and machine learning modeling techniques, and is capable of real-time, dynamic and personalized prediction and analysis of an individual's psychological state. Experimental results show that the proposed system exhibits high prediction accuracy and stability on multiple public and real datasets, and has good application prospects. The research in this paper provides an efficient and objective technical path for mental health screening, and also lays the foundation for the subsequent exploration of the application of artificial intelligence in the psychosomatic field.