查看文章

Mental health disorders, including anxiety, depression, and stress, profoundly impact individuals' well being and necessitate effective early detection for timely intervention. This research investigates the predictive capabilities of machine learning algorithms in assessing anxiety, depression, and stress levels based on questionnaire derived scores. Utilizing a dataset comprising self reported scores obtained through a tailored questionnaire designed for mental health assessment, we delve into the application of Decision Trees, Naive Bayes, Support Vector Machines (SVM), and Random Forests for prediction. Data pre processing involved comprehensive cleaning, encoding categorical variables, and careful feature selection, ensuring the relevance of features in the predictive models. Each algorithm underwent individual implementation, wherein we scrutinized their performances in predicting mental health …