AI Models Achieve Over 98% Accuracy in Predicting Mental Health Conditions Using Psychometric Data
Artificial intelligence systems have demonstrated exceptional capability in identifying depression, anxiety, and stress disorders according to new research. The study utilized validated psychometric data from nearly 40,000 participants to train multiple machine learning models, with support vector machines achieving accuracy rates exceeding 98% across all three conditions.
Breakthrough in Mental Health Prediction
Artificial intelligence systems have demonstrated remarkable accuracy in predicting common mental health conditions using standardized psychological assessments, according to recent research published in Scientific Reports. The study, which analyzed responses from 39,775 participants, indicates that machine learning algorithms can identify depression, anxiety, and stress with accuracy rates exceeding 98% using the Depression Anxiety Stress Scales-42 (DASS-42) questionnaire combined with demographic information.