Detecting Anxiety via Machine Learning Algorithms: A Literature Review

• Published in: IEEE transactions on emerging topics in computational intelligence Vol. 9; no. 4; pp. 2634 - 2657
• Main Authors: Tayarani-N., M.-H., Shahid, Shamim Ibne
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.08.2025; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Accuracy; affective computing; Algorithms; Anxiety; Anxiety disorder; Anxiety disorders; artificial intelligence; Depression; Feature extraction; Heart rate; Literature reviews; Machine learning; Machine learning algorithms; mental disorder; Mental health; Prediction algorithms; Prediction models; Signal processing algorithms; Sleep; social signal processing; State-of-the-art reviews; Systematic literature review
• ISSN: 2471-285X; 2471-285X
• DOI: 10.1109/TETCI.2025.3543307

Recent machine learning (ML) advances have opened up new possibilities for addressing various challenges. Given their ability to tackle complex problems, the use of ML algorithms in diagnosing mental health disorders has seen substantial growth in both the number and scope of studies. Anxiety, a major health concern in today's world, affects a significant portion of the population. Individuals with anxiety often exhibit distinct characteristics compared to those without the disorder. These differences can be observed in their outward appearance-such as voice, facial expressions, gestures, and movements-and in less visible factors like heart rate, blood test results, and brain imaging data. In this context, numerous studies have utilized ML algorithms to extract a diverse range of features from individuals with anxiety, aiming to build predictive models capable of accurately identifying those affected by the disorder. This paper performs a comprehensive literature review on the state-of-the-art studies that employ machine learning algorithms to identify anxiety. This paper aims to cover a wide range of studies and categorize them based on their methodologies and data types used.