Evaluating the Impact of Sahaja Yoga Meditation on Anxiety and Depression Using Machine Learning Models

Abstract

Anxiety and depression affect people, around the world. The need, for effective, accessible non‑drug treatments is clear. Sahaja Yoga meditation is a practice that can lower stress and boost emotional health. The study examines how Sahaja Yoga meditation changes anxiety and depression using machine learning models. The researchers collected a dataset from participants who practiced Sahaja Yoga meditation over a period. The data were gathered over weeks. It observed that the participants reported anxiety and less depression after practicing Sahaja Yoga meditation. We did health assessments before the intervention and, after the intervention using the scales GAD-7 and PHQ-9. It applied machine learning algorithms. The machine learning algorithms included Random Forest, Support Vector Machines and Gradient Boosting. It used the machine learning algorithms to analyze and predict improvements in health. It observed drops, in anxiety scores and depression scores after practice of Sahaja Yoga. The machine learning models identified factors that helped health improvements. The machine learning models found the factors that mattered. These findings suggest that combining meditation practices with data-driven methods can enhance mental health monitoring and lead to personalized well-being interventions. This work also demonstrates how machine learning can objectively confirm the therapeutic benefits of Sahaja Yoga meditation.