University of Twente Student Theses


Predicting anxiety disorders : the key to data driven policy

Demmink, M.M.R. (2023) Predicting anxiety disorders : the key to data driven policy.

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Abstract:Anxiety disorders affect more than one out of seven people in the Netherlands, making it the most prevalent mental disorder in the country. In this study, data on the prevalence of anxiety disorders and social-economic and environmental factors were collected from 325 Dutch municipalities from 2015-2020. Machine learning techniques were then used to identify risk and protective factors associated with anxiety disorders and make predictions about future prevalence from 2021-2026. Risk and protective factors for anxiety disorders are identified with linear regression, lasso regression, and a neural network. The neural network was able to predict the proportion of anxiety disorder for each municipality from 2015-2026, highlighting the disease burden. The study provides insights into the potential burden of anxiety disorders and the need for intervention, and it guides the development of effective strategies to reduce its prevalence and improve the quality of life for individuals affected by this mental health disorder.
Item Type:Essay (Master)
KPMG, Amstelveen, The Netherlands
Faculty:BMS: Behavioural, Management and Social Sciences
Subject:50 technical science in general
Programme:Industrial Engineering and Management MSc (60029)
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