Abstract—The Depression, Anxiety, and Stress Scales (DASS) are 3-factor paradigms; the research is about understanding an individual’s instant, psychic response to the epidemic in evaluating their mental health. Anxiety, stress, and depression in adults over the age of eighteen are the primary subjects of this study. In computing behaviourism, it’s critical to analyse individuals’ emotional responses to strain and forecast how they will respond. 39,775 responses from individuals were obtained for the research data who took part in experiments between 2017 and 2019. When connecting attributes to anxiety levels, nearly seven distinct advanced Machine Learning (ML) based classifying algorithms were applied for experimentation. Further Artificial Intelligence (AI) techniques were used to substantially increase the accuracy of the research work. Ada-boost categorising with Support Vector Machine (SVM) base and SVM classification algorithms had a maximum predictive performance of 96% in Machine Learning models. The model acquired the highest results and the quickest time to prepare the entire collection after using Deep Learning (DL) algorithms.
 
Keywords—DASS-21, Adaboost, support vector machine classifier, psychology, convolution neural network, naïve bayes


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