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Abstract

Panic disorder, marked by recurrent and unexpected panic attacks, significantly impairs daily functioning and overall well-being. Early detection is crucial to improving patient outcomes, yet traditional diagnostic methods often delay timely identification. This study investigates the application of machine learning (ML) techniques for the early detection of panic disorder and the identification of key features that contribute to its development. Utilising a comprehensive dataset of clinical and physiological data including demographics, symptoms, and vital signs from individuals with and without panic disorder, multiple ML classification algorithms were trained, tested, and evaluated. The ensemble voting feature selection method was employed to pinpoint the most relevant predictors of panic disorder. Among the models tested, the Extra Tree Bagging Ensemble ML model demonstrated exceptional performance, achieving 99.8% accuracy, along with high sensitivity and precision. Feature significance analysis revealed critical physiological and psychological factors associated with panic vulnerability, offering valuable insights into the disorder’s underlying mechanisms. This research underscores the potential of ML-based approaches in enabling early detection of panic disorder, paving the way for personalised prevention and intervention strategies. The findings highlight the importance of integrating advanced computational techniques in mental health diagnostics to enhance accuracy and timeliness in identifying panic disorder.

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