Longitudinal Prediction of Mental Health Outcomes in Vulnerable Youth using Machine Learning

Longitudinal Prediction of Mental Health Outcomes in Vulnerable Youth using Machine Learning | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Longitudinal Prediction of Mental Health Outcomes in Vulnerable Youth using Machine Learning Esmeralda Ruiz Pujadas, Covadonga M. Díaz-Caneja, Dejan Stevanovic, and 11 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4742021/v2 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 07 Oct, 2025 Read the published version in Cognitive Computation → Version 2 posted You are reading this latest preprint version Show more versions Abstract Mental illnesses affect almost 15% of the world's population, with half of the cases emerging before age 14. Improved methods for predicting mental distress among adolescents, particularly in vulnerable populations, are needed. This study utilized traditional machine learning techniques to predict mental health status at age 17. We assessed the correlates of mental health outcomes in a sample of 632 adolescents with general mental distress (i.e., total difficulties score of 17 or higher) at age 11, who participated in the UK Millennium Cohort Study. Predictors measured at ages 11 and 14 were included in the analysis. Mental health status at age 17 was best predicted using a Balanced Random Forest model (AUC 0.75). Explainability techniques enabled the identification of several critical factors, such as school environment, emotional distress, sleep patterns, patience, and social network at ages 11 or 14, which were able to differentiate participants with poor or good mental health outcomes at age 17. Individuals experiencing persistent mental distress between the ages 11 and 17 were most likely to suffer from unhappiness and academic struggles. Our results point to potentially modifiable factors associated with the progression of mental distress in adolescents at high risk. These factors could pave the way for improved early intervention and preventive strategies for vulnerable young people during adolescence. Full Text Additional Declarations No competing interests reported. Supplementary Files SupplementaryFile.docx Cite Share Download PDF Status: Published Journal Publication published 07 Oct, 2025 Read the published version in Cognitive Computation → Version 2 posted You are reading this latest preprint version Show more versions Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4742021","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":508145324,"identity":"87e53e18-bc53-46bf-9e50-08b1cb8365bc","order_by":0,"name":"Esmeralda Ruiz Pujadas","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA50lEQVRIiWNgGAWjYPACC8YGEJXAYMNgAKQliNAiAdOSRqoWBobDhLXw859OfFxRIyHb38D7TOLBn/OJ2xmYD97mwaNFckbuZsMzxySMZxxgN5NI4LmduLOBLdkanxaDG7zbJBvYJBIbDrCxSSRI3E7ccIDHTBqfFvvzZ7f/bPgnkTgfrMXgHFAL/ze8WgwYcrcxNrZJAFWCtCQcANnChleLxI3czZKNfRLGGw+zMVskHEg23tnMZmw5B48W/v6zGz82fLORnXe8jfHmjz92stvZmx/eeINHCwIwYzBGwSgYBaNgFJANACeDSx6Uj31wAAAAAElFTkSuQmCC","orcid":"","institution":"Universidad de Barcelona","correspondingAuthor":true,"prefix":"","firstName":"Esmeralda","middleName":"Ruiz","lastName":"Pujadas","suffix":""},{"id":508145327,"identity":"74e1ddff-f25b-4604-8680-e33b542b2f73","order_by":1,"name":"Covadonga M. 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