ArticleBioengineering (Basel, Switzerland)2023
Machine Learning-Based Prediction of Mental Well-Being Using Health Behavior Data from University Students.
Article in Bioengineering (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.
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Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
12 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Machine Learning for the Analysis of Healthy Lifestyle Data: Scoping Review and Guidelines.JMIR human factors · 2026Guideline
- An explainable machine learning framework for analyzing and predicting mental health problems among university students in Bangladesh.Scientific reports · 2026Article
- Explainable AI for Well-Being Prediction From Lifestyle Data: 2-Study Design.JMIR mental health · 2026Article
- Article
- Predicting college students' exercise dependence: a machine learning approach.Frontiers in psychology · 2026Article
- Machine learning-based predictive modelling of mental health in Rwandan Youth.Scientific reports · 2025Article
- Enhancing Psychological Well-Being Assessment Through Data Mining: A Case Study from Thailand.European journal of investigation in health, psychology and education · 2025Article
- Predicting mental health disparities using machine learning for African Americans in Southeastern Virginia.Scientific reports · 2025Article
- PERMA-guided multi-topology graph neural networks for cross-cultural student well-being prediction.PloS one · 2025Article
- Article
- Understanding the impact of sisu on workforce and well-being: A machine learning-based analysis.Heliyon · 2024Article
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7 authors.
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Abstract
backgroundSince the onset of the COVID-19 pandemic in early 2020, the importance of timely and effective assessment of mental well-being has increased dramatically. Machine learning (ML) algorithms and artificial intelligence (AI) techniques can be harnessed for early detection, prognostication and prediction of negative psychological well-being states.
methodsWe used data from a large, multi-site cross-sectional survey consisting of 17 universities in Southeast Asia. This research work models mental well-being and reports on the performance of various machine learning algorithms, including generalized linear models, k-nearest neighbor, naïve Bayes, neural networks, random forest, recursive partitioning, bagging, and boosting.
resultsRandom Forest and adaptive boosting algorithms achieved the highest accuracy for identifying negative mental well-being traits. The top five most salient features associated with predicting poor mental well-being include the number of sports activities per week, body mass index, grade point average (GPA), sedentary hours, and age.
conclusionsBased on the reported results, several specific recommendations and suggested future work are discussed. These findings may be useful to provide cost-effective support and modernize mental well-being assessment and monitoring at the individual and university level.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.