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Measuring the Impact of Meditation on Academic Stress Levels in UG Students Using Machine Learning Models

Srikanta Parida, Dr N. Suresh, Pujith Yerapathi, K Ponnanna K, Adithya Vinod

International Conference on Computing for Sustainable Global Development April 8, 2026 DOI: 10.23919/indiacom70271.2026.11525795 (opens in new tab)

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Study at a glance

AI-extracted from the abstract
Characteristics Observational cohort with machine learning and causal inference Peer reviewed
Sample size 680
Population College students
Intervention Meditation
Topics Meditation
Keywords Education
Key findings Meditation reduced academic stress, especially among students with medium baseline stress. Random Forest predicted outcomes with 88.24% accuracy, outperforming KNN (77.21%) and SVM (63.97%). Age, meditation frequency, and baseline stress were the strongest predictors of stress reduction.

Abstract

Using a combination of machine learning and causal inference techniques, this study explores the ways in which meditation can help college students manage their academic stress. In order to predict stress reduction based on initial psychological responses and demographic characteristics, we examined data from more than 680 students. To classify stress levels and forecast post-meditation outcomes, three machine learning algorithms were evaluated: Random Forest, Support Vector Machine (SVM), and K-Nearest Neighbors (KNN). With 88.24 % accuracy, 89.05 % precision, and 88.24 % recall, the Random Forest model outperformed the others. Accuracy values for KNN and SVM were 77.21 % and 63.97 %, respectively. The best indicators of stress reduction were age, meditation frequency, and baseline stress. The treated group experienced a significant reduction in stress, particularly among those with medium baseline stress, according to causal analysis (Causal Forest and T-Learner). These results were validated by K-Means clustering, which was consistent with the machine learning outcomes. In conclusion, machine learning models, especially Random Forest, are capable of predicting individual results, and meditation successfully lowers academic stress in specialized circumstances. This work demonstrates that combining predictive modeling with causal inference provides both accurate stress prediction and meaningful estimates of meditation's impact across different stress levels.

Comparable studies

Other observational and cohort studies on meditation, most cited first.

Study Year Design Participants
Using Self-Report Assessment Methods to Explore Facets of Mindfulness Undergraduate students 2006 Observational cohort
Effectiveness of a meditation-based stress reduction program in the treatment of anxiety disorders Patients with generalized anxiety disorder or panic disorder with or without agoraphobia 1992 Observational cohort n = 22
Meditation experience is associated with increased cortical thickness Participants with extensive Insight meditation experience and matched controls 2005 Observational cohort n = 20
Meditation experience is associated with differences in default mode network activity and connectivity Experienced meditators and matched meditation-naive controls 2011 Observational cohort
Long-term meditators self-induce high-amplitude gamma synchrony during mental practice Long-term Buddhist practitioners and controls 2004 Observational cohort

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