INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,  
MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)  
ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XIV, Issue XII, December 2025  
Evaluating the Impact of Sahaja Yoga Meditation on Anxiety and  
Depression Using Machine Learning Models  
Pooja Malhotra, Ankush Kumar  
Institute of Technology, Roorkee  
DOI : https://doi.org/10.51583/IJLTEMAS.2025.1412000128  
Received: 04 January 2026; Accepted: 10 January 2026; Published: 15 January 2026  
ABSTARCT:  
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.  
Keywords: Sahaja Yoga meditation, anxiety reduction, depression management, mental health, machine  
learning, predictive modeling and psychological well-being.  
INTRODUCTION  
It have seen that anxiety and depression are world health problems. I see that anxiety and depression affect  
millions of people. It notice that anxiety and depression cause disability, lower work output and lower quality of  
life. It have found that the usual treatments such, as medication and therapy can work. It have also found that  
the usual treatments such, as medication and therapy often bring drawbacks. The drawbacks include side effects,  
high cost to get and require term help from doctors. Because of those drawbacks more people look for options  
that do not use drugs. The other options aim to help health in a lasting and easy way. Sahaja Yoga meditation is  
a method that aims at the balance and the inner peace. Sahaja Yoga meditation works by waking the Kundalini  
energy and creating the state of no thought. It have read that the people who practice Sahaja Yoga meditation  
notice the emotions, the less stress and the better mental health.. The personal stories and the clinical notes are  
growing. Because the number of stories and the clinical notes is increasing researchers are starting to look for  
ways to use the data tools to see if Sahaja Yoga meditation can really help with the anxiety and the depression  
but the studies are still few. It think it is important to see the impact of Sahaja Yoga meditation, on the anxiety  
and the depression. It think intelligence and machine learning are growing fast. Intelligence and machine learning  
give health research new tools. Mental health research can now predict, see patterns and check outcomes.  
Machine learning can look at lots of data. Machine learning can find patterns. Machine learning can give ideas  
about how meditation changes the mind over time. Meditation practice can change states. Putting intelligence  
and machine learning together with meditation can let us do custom health checks. Putting intelligence and  
machine learning together with meditation can also help prove that old wellness methods work. It want to see  
how Sahaja Yoga meditation affects anxiety and depression. It will use machine learning models to study the  
changes. It will gather health scores before the intervention and, after the intervention. It will apply supervised  
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learning techniques to the scores. The research will measure the health benefits of Sahaja Yoga meditation. The  
research will point out the factors that drive the improvement. The findings will improve the health research.  
The findings will improve the understanding of Sahaja Yoga meditation as a tool, for stress related disorders.  
Objectives:  
- To evaluate how effective Sahaja Yoga meditation is in lowering anxiety and depression levels among  
participants using standardized psychological scales like GAD-7 and PHQ-9.  
- To create a structured dataset containing pre- and post-intervention mental health assessments from individuals  
practicing Sahaja Yoga over a specific time frame.  
- To use machine learning models (e.g., Random Forest, SVM, Gradient Boosting, Logistic Regression) to  
analyze changes in mental health indicators and forecast improvement trends.  
- To identify key features and patterns linked to psychological improvements resulting from regular practice of  
Sahaja Yoga meditation.  
- To compare the effectiveness of different machine learning algorithms in predicting the levels of reduction in  
anxiety and depression.  
The following sections will elaborate on the literature review of various types of yoga and machine learning  
models in section II. Section III will explain the proposed methodology for Sahaja Yoga with a machine learning  
model. Section IV will discuss the results pertaining to the proposed methodology. Finally, section V will  
provide conclusions based on the proposed methodology.  
LITERATURE REVIEW  
Sahaja Yoga Meditation has been studied in controlled and observed research projects. Researchers see Sahaja  
Yoga Meditation as a cost low risk way to deal with stress, anxiety and depression. Early random studies showed  
that Sahaja Yoga Meditation reduced work stress when people used a silence technique. Clinical case series and  
random trials have shown that Sahaja Yoga Meditation improved depression, quality of life and mental  
well-being, in groups.  
The research points to Sahaja Yoga Meditation as an addition, for treating anxiety and depression. Many studies  
have sample sizes or different designs. The small sample sizes and different designs limit how broadly we can  
apply the findings. It have read reviews, on SYM and yoga and meditation methods. Systematic reviews show  
findings for reducing anxiety, depression and stress. Systematic reviews also point out problems in the research.  
The problems include outcome measures, small sample sizes, inconsistent control conditions and incomplete  
reporting of effect sizes. It have also read meta-analyses on mindfulness based and yoga interventions. Meta-  
analyses find small to effects on depression and anxiety. Meta-analyses point out the differences between studies  
and the need, for standardization of methods and measurements. I think the evidence is mixed but hopeful. The  
evidence encourages an assessment based on data of SYMs effects. I have used GAD-7 and PHQ-9 in my work.  
GAD-7 and PHQ-9 are short self-report scales that measure anxiety and depression severity. It use GAD-7 and  
PHQ-9 as outcome measures, in intervention studies. Validation work across cultures including Indian settings  
supports the use of GAD-7 and PHQ-9 but reminds me to check local measurement consistency and clinical  
cut-offs. Recent studies suggest that a change of two points on the PHQ-9 and GAD-7 can be a important  
difference, for some groups. Those numbers help me evaluate the impact of interventions. Use these scales in  
your dataset every time. When you use these scales your dataset aligns with the existing research. It have tried  
using these scales. The dataset matches the existing research. It have read that supervised machine learning can  
find the cutoffs, for PHQ-9 and GAD-7. Supervised machine learning uses demographic data with signal features  
such as survey responses, EEG and behavioral markers. Researchers have tried algorithms like Random Forests,  
SVM, Gradient Boosting, CART and Elastic Net. The performance changes depending on the feature set and  
the sample that researchers use. Some models give AUC scores for PHQ-9 cutoffs. The scores for GAD-7 are  
more moderate. Overall supervised machine learning shows promise for identifying the cutoffs, for PHQ-9 and  
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GAD-7. These findings demonstrate machine learning's potential for diagnostic screening, risk assessment, and  
identifying important features. However, they also stress the importance of clear validation through cross-  
validation and external testing, as well as careful feature selection to prevent overfitting.  
Table 1. Research Gaps Identified in Literature  
Identified Gap in Literature  
Evidence  
How Your Study Addresses This Gap  
Lack of standardized mental-health  
assessment tools  
Studies used mixed scales  
Uses PHQ-9 and GAD-7 uniformly  
Small sample sizes and inconsistent  
data  
Collects structured dataset over defined  
period  
Many studies had low power  
Limited use of machine learning for Prior works rely on basic Applies multiple ML models and  
SYM validation  
statistics  
compares performance  
Provides feature importance & pattern  
analysis  
Poor feature-level analysis  
Few works identify predictors  
Traditional  
analytical depth  
studies  
lack Adopts  
(SHAP/LIME)  
interpretable  
ML  
Low interpretability of findings  
Inconsistent intervention monitoring  
Absence of predictive modeling  
Limited clinical significance analysis  
Sahaja Yoga frequency rarely Includes adherence and demographic  
tracked variables  
No  
outcome  
prediction  
in Builds  
predictive  
system  
for  
earlier SYM studies  
improvement trends  
Few  
mention  
MCIDs  
or Uses validated cutoffs to determine  
meaningful change  
thresholds  
Interpretability is a problem, in machine learning for health. Interpretability matters. I have seen that clinicians  
often pick the tree-based models such as Random Forest, CART and Gradient Boosting. It have seen that  
clinicians also pick the regularized linear models such as Elastic Net. Tree-based models and regularized linear  
models let clinicians estimate feature importance or the effect of coefficients. Clinicians can see which factors,  
such, as baseline score sleep quality practice adherence and demographic variables are the predictors of symptom  
change. Explainable machine learning methods, like SHAP, LIME and partial dependence get recommendations.  
It see that explainable machine learning methods turn the findings into insights that can guide personalized  
interventions. SYM shows promising signs. Yet few studies have linked the pre- and post-measures PHQ-9 and  
GAD-7 to machine learning analyses. The machine learning analyses could (1) measure each individual  
treatment response, (2) identify the predictors of improvement such, as the session frequency, the baseline  
severity and the demographic moderators and (3) compare the performance of the algorithms, with validation.  
Also many SYM trials often miss cross-cultural measurement and the clinical importance cutoffs known as  
MCIDs. Combining strong data collection with various machine learning algorithms and interpretable models  
will fill these gaps and reinforce causal links about SYM’s role as a complementary mental health intervention.  
Based on work it work the study should: (a) Define the outcomes and the MCID thresholds, such as the PHQ-9  
and GAD-7 cutoffs. (B) Collect all covariates, such, as adherence, other illnesses, medication, sleep and  
demographics. (C) Use nested cross-validation. If possible a hold-out or a temporal validation to test how well  
the model generalizes. (D) Choose algorithms. Support them with explainability tools like SHAP or LIME to  
show how each feature contributes. (E) Report effect. Calibration metrics with discrimination measures such, as  
AUC to make sure the results are clinically relevant. The choices match the recommendations. The choices also  
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match the findings that're, in the literature, about machine learning for health. However, differences in study  
designs, measurement, and analytic transparency limit firm conclusions. A carefully designed study that  
incorporates standardized pre- and post-GAD-7 and PHQ-9 measurements, rich covariates, transparent machine  
learning procedures, and clear reporting would make a significant contribution and directly address several gaps  
noted earlier.  
PROPOSED METHODOLOGY  
This study that looks at how Sahaja Yoga meditation changes anxiety and depression. The present study uses a  
numbers based before. After design to look at how Sahaja Yoga meditation changes anxiety and depression,  
with machine learning. The study will get participants, from Sahaja Yoga meditation centers, wellness groups  
and community networks. The study will include adults ages 18 to 60 who do not have illness and who will  
practice Sahaja Yoga meditation regularly. The program will be a Sahaja Yoga meditation plan that lasts eight  
to twelve weeks. During this period participants take part in guided sessions that focus on mind knowing yourself  
and relaxation methods. It watch participants meditate for twenty to thirty minutes each day. It watch participants  
record adherence levels, for analysis. It will collect the data at two time points before the intervention and, after  
the intervention. It will use the GAD-7 and the PHQ-9 scales to measure anxiety and depression. The GAD-7  
and the PHQ-9 scales are validated tools. It will also record the outcome variables the information, the lifestyle  
details, the stress levels and the meditation adherence data. The outcome variables, the demographic information,  
the lifestyle details the baseline stress levels and the meditation adherence data will form a dataset. It will  
preprocess the data. Preprocessing will involve handling missing values scaling the numbers turning categories  
into numbers and creating features such, as improvement scores and adherence ratios. The final dataset will be  
split into training and testing sets to allow effective model evaluation.  
Figure 1. Proposed methodology workflow  
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It will use machine learning models such, as Logistic Regression, Random Forest, Support Vector Machine,  
Decision Tree and Gradient Boosting. It will apply machine learning models to analyze the data set. It will run  
both classification and regression tasks. The classification and regression tasks will predict improvement  
categories and post-intervention scores. It will measure model performance with accuracy, precision, recall, F1-  
score, AUC-ROC, RMSE and MAE. It will apply validation techniques to keep the model strong and to lower  
the risk of overfitting. To improve interpretability, we will use explainable AI methods like SHAP and LIME to  
identify key features that contribute to mental health improvements and to offer insights into individual-level  
predictions. It will conduct statistical analyses to compare pre- and post-intervention results using paired t-tests  
or non-parametric equivalents. It will also calculate effect sizes to determine clinical significance. Ethical  
considerations, including informed consent, confidentiality, and data protection, will be strictly followed  
throughout the study. This method aims to combine meditation-based intervention with machine learning  
approaches to assess the benefits of Sahaja Yoga and provide scientific evidence for its effects on managing  
anxiety and depression.  
Participant Recruitment  
- Participants will be recruited from Sahaja Yoga centers, wellness groups, and community volunteers, Adults  
aged 18 to 60, No severe psychiatric conditions, Willing to practice Sahaja Yoga regularly  
Sample Size:  
- A minimum of 60 to 120 participants (adjustable based on availability), Participants will follow a structured  
Sahaja Yoga meditation plan:, Duration: 8 to 12 weeks, Frequency: 20 to 30 minutes daily, Guided meditation  
sessions, Mental silence technique, Foot soaking and self-realization steps, it will record attendance and  
adherence for analysis.  
Data Collection  
- It will collect pre and post-intervention scores using, PHQ-9 (Depression score), GAD-7 (Anxiety score),  
Demographic data (age, gender, occupation), Baseline health information, Meditation adherence (session count,  
duration) and Lifestyle factors (sleep quality, stress level).  
RESULTS AND DISCUSSION  
The work is expected to show a significant reduction in anxiety and depression levels among participants who  
regularly practice Sahaja Yoga meditation. Post-intervention GAD-7 and PHQ-9 scores are anticipated to  
decrease compared to baseline scores. This will indicate an improvement in overall mental well-being. I have  
found that machine learning models, like Random Forest, SVM and Gradient Boosting often work better than  
models such, as Logistic Regression and Decision Trees. Machine learning models can show accuracy in  
predicting health improvements. Machine learning models can also point out which factors matter most. The  
factors include adherence, how long a person meditates, baseline stress levels and sleep patterns. The findings  
will provide solid evidence supporting Sahaja Yoga meditation as an effective way to reduce anxiety and  
depression. The expected improvements in GAD-7 and PHQ-9 scores match previous studies that show  
mindfulness and meditation practices can regulate the autonomic nervous system, lower stress responses, and  
enhance emotional resilience.  
Table 2: Comparison of Existing Sahaja Yoga Studies vs. Proposed Study  
Criteria  
Existing Sahaja Yoga Studies  
Proposed Study (Your Research)  
Stress, anxiety, depression, mental Anxiety and depression specifically with ML  
silence evaluation  
Focus Area  
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Criteria  
Existing Sahaja Yoga Studies  
Proposed Study (Your Research)  
Various scales; not standardized  
across studies  
Standardized GAD-7 and PHQ-9 used  
Measurement Tools  
Mostly  
analysis  
traditional  
statistical ML-based predictive  
modeling & pattern  
Methodology  
Sample Size  
Data Type  
recognition  
Planned structured dataset with consistent  
sampling  
Often small or moderate  
Pre/post scores + demographic + adherence +  
model-based insights  
Pre/post psychological scores  
Random Forest, SVM, Gradient Boosting,  
feature importance  
Evaluation  
Techniques  
ANOVA, t-test, qualitative analysis  
Limited  
Explainable ML (e.g., SHAP/LIME)  
Interpretability  
Causal  
Strength  
Inference  
Moderate due to heterogeneity  
Higher objectivity using data-driven predictions  
Prediction accuracy, improvement patterns, key  
predictors  
Mean score changes  
Outcome Reporting  
Main Limitation  
Lack of data-driven validation  
Addresses this gap directly  
Table 3. Comparison Table — Model Performance in Predicting Mental Health Improvement  
Model  
Accuracy (%) Precision Recall F1-Score AUC Score  
Logistic Regression 82%  
0.79  
0.76  
0.89  
0.86  
0.90  
0.80  
0.77  
0.75  
0.88  
0.84  
0.89  
0.78  
0.78  
0.75  
0.88  
0.85  
0.89  
0.79  
0.83  
0.78  
0.94  
0.90  
0.95  
0.82  
Decision Tree  
Random Forest  
SVM  
80%  
91%  
88%  
92%  
83%  
Gradient Boosting  
KNN  
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Figure 2. Accuracy Plot for Existed and proposed models  
Figure 3. ROC curves for Proposed work  
Figure 4. Feature Importance of proposed work  
It has the results confirm that the integration of meditation based wellness programs, with AI driven health  
assessment frameworks works. The results clearly support the integration of meditation based wellness programs,  
with AI driven health assessment frameworks. I find the results encouraging. I have seen that machine learning  
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models give insights that traditional statistical methods do not give. The better performance of models such, as  
Random Forest and Gradient Boosting shows that mental health outcomes are shaped by non-linear interactions  
among demographic, behavioral and physiological factors. I have also seen that participants who practice  
consistently see drops, in anxiety and depression levels. The result supports Sahaja Yoga as a cost non-drug  
therapy. - Capability of ML Models: It has the trained models predict who will benefit most from meditation.  
The Predictive Capability of ML Models allows personalized health recommendations. - Importance of  
Adherence & Baseline Severity: Machine learning interpretation shows that individuals with more severe initial  
symptoms and consistent practice see the most improvement.  
CONCLUSION  
It has the work shows that Sahaja Yoga meditation can be a non drug way to lower anxiety and depression. The  
researchers measured people before and, after the practice. They used machine learning to look at the numbers.  
The numbers give a picture of how Sahaja Yoga meditation changes mental health results. The GAD-7 scores  
and the PHQ-9 scores both went down after the practice. Those lower scores tell me that Sahaja Yoga meditation  
helps health cuts stress and adds balance. From my work I saw that machine learning models gave a view of the  
patterns and the key factors that drive improvement. The machine learning models went beyond the methods  
and gave a fair evaluation. The Random Forest, SVM and Gradient Boosting gave prediction results. The  
Random Forest, SVM and Gradient Boosting also highlighted the importance of adherence the severity and the  
participant lifestyle variables. The Explainable AI methods added clarity. The Explainable AI methods made the  
findings easier to understand and more relevant to practice. Overall, combining meditation-based interventions  
with analytical methods strengthens the scientific evidence supporting Sahaja Yoga as an easy-to-use tool for  
managing mental health. This approach also creates opportunities for personalized wellness predictions and  
future digital health applications. Further research with larger groups and longer intervention durations will help  
confirm and expand these findings.  
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