Detecting psychological uncertainty using machine learning

Psychological uncertainty encompassing findings such as anxiety, depression, and bipolar disorder poses significant challenges for timely diagnosing and successful treatment. Traditional diagnostic methods often depend on subjective assessments, leading to inconsistencies and potential biases. This research finds the applications of machine learning techniques to identify Psychological uncertainty with greater accuracy and objectivity. It uses a comprehensive dataset of healthcare records, standardized mental health tests, and social media activity to train multiple machine learning models, including Support Vector Machines (SVM), Random Forests, and Convolutional Neural Networks (CNN). The models were assessed based on their accuracy, precision, recall, and F1-score. The results shows that the Random Forest model had the highest accuracy (87%), with major predictive characteristics including social media sentiment ratings, frequency of healthcare visits, and physiological data such as heart rate variability. These findings indicate that machine learning can considerably improve the detection of psychological ambiguity, providing a reliable alternative to traditional diagnostic methods. The work highlights the potential for incorporating machine learning into mental health diagnostics to enable earlier interventions and individualized treatment programs, ultimately improving patient outcomes. Future research should focus on increasing datasets and using real-time monitoring technology to improve these predictive models.