UH Biocomputation Group - Mental healthhttp://biocomputation.herts.ac.uk/2025-03-25T12:28:32+00:00Detecting Anxiety via Machine Learning Algorithms: A Literature Review2025-03-25T12:28:32+00:002025-03-25T12:28:32+00:00Shamim Ibne Shahidtag:biocomputation.herts.ac.uk,2025-03-25:/2025/03/25/detecting-anxiety-via-machine-learning-algorithms-a-literature-review.html<p class="first last">Shamim Ibne Shahid's Journal Club session where he will talk about "Detecting Anxiety via Machine Learning Algorithms: A Literature Review".</p>
<p>On this week's Journal Club session, Shamim Ibne Shahid will talk about his review paper "Detecting Anxiety via Machine Learning Algorithms: A Literature Review".</p>
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<p>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.</p>
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<p>Papers:</p>
<ul class="simple">
<li>M. Tayarani-N., S. Shahid, <a class="reference external" href="https://doi.org/10.1109/TETCI.2025.3543307">"Detecting Anxiety via Machine Learning Algorithms: A Literature Review"</a>, 2025, IEEE Transactions on Emerging Topics in Computational Intelligence, 1--24</li>
</ul>
<p><strong>Date:</strong> 2025/03/28 <br />
<strong>Time:</strong> 14:00 <br />
<strong>Location</strong>: SP3011 & online</p>