A Machine Learning Framework for Automated Detection of Perceived Stress in Female Students

Heartfulness meditation practiced for 20 weeks significantly reduces perceived stress and increases satisfaction with life among female students, regardless of age. Machine learning analysis of survey data from two age groups detected stress with up to 66.88% accuracy using a support vector machine classifier. The exploratory data analysis showed a meaningful decrease in stress levels and an increase in life satisfaction after the meditation period. The findings suggest that this meditation practice is an effective non-pharmaceutical approach to improving mental health.

Study at a glance

Abstract

Stress is a mental state induced by difficult circumstances. COVID-19 has severely impacted the level of stress and mental health of individuals around the globe. Pharmaceutical treatment (hypnotic or sedative drugs) has shown its ineffectiveness with several unwanted side effects to reduce mental stress. Mindfulness practices have proven their potency to reduce perceived stress (PS) and enhance satisfaction with life (SWL) without any negative consequences. This study aims to detect PS in female students and study the impact of Heartfulness meditation in reducing it. In this work, statistical exploratory data analysis (EDA) is utilized to evaluate the effectiveness of Heartfulness meditation. PS and SWL scores of Week 0 and Week 20 data are recorded from two different age groups of female students and analyzed to detect stressed students using machine learning modules. The highest PS detection accuracy 66.88% and F1-score 65.30% for group-1 are obtained using the support vector machine classifier. EDA indicates that there is a significant decrease in the PS levels and an increase in the SWL after 20 weeks of Heartfulness meditation. The results of this study indicate that the practice of Heartfulness meditation offers significant advantages to female students, regardless of age. Moreover, it was found that the practice effectively reduces stress levels among the participants. Received: 18 November 2025 | Revised: 19 May 2026 | Accepted: 27 May 2026 Conflicts of Interest The author declares that he has no conflicts of interest to this work. Data Availability Statement Data are available from the corresponding author upon reasonable request. Author Contribution Statement Kapil Gupta: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Writing – original draft, Writing – review & editing, Visualization.