Harnessing Machine Learning for Accurate Prediction of Mental Health Conditions based on DASS-42 scores

The increasing global prevalence of mental health conditions, coupled with their profound impact on individual well-being, necessitates the development of efficient diagnostic tools. Traditional methods of mental health assessment, such as the Depression, Anxiety, and Stress Scales (DASS-42), rely on manual interpretation of questionnaire scores, which may be limited in detecting complex patterns. This study proposes harnessing the power of machine learning (ML) to improve the accuracy and efficiency of mental health condition predictions based on DASS-42 scores. By leveraging various ML algorithms, including Random Forest, Decision Trees, and Nearest Neighbour, we aim to predict mental health outcomes such as depression, anxiety, and stress with greater precision. The DASS-42 dataset used in this study contains responses from individuals across diverse demographic backgrounds. Feature engineering is applied to extract meaningful attributes, while the models are trained and evaluated on labelled data indicating the presence or absence of mental health conditions. Cross-validation and hyperparameter tuning are employed to optimize the performance of the models, and metrics such as accuracy, precision, recall, and F1 score are used to assess their predictive capabilities. Initial results demonstrate that machine learning models, particularly ensemble methods like random forests, outperform traditional statistical methods in predicting mental health outcomes. The incorporation of ML not only improves diagnostic accuracy but also has the potential to streamline mental health screening processes in clinical and non-clinical settings. This research highlights the significant role that machine learning can play in enhancing the identification and management of mental health conditions, thereby contributing to more effective interventions and personalized treatment plans. Future work will focus on refining models by integrating additional psychological and physiological data to further increase predictive power.