🌐 My Research Journey: Stress and Anxiety Analysis Using Machine Learning 🧠📊

Hello readers! 👋
I'm thrilled to share an exciting milestone in my academic and research journey. I recently had the honor of presenting my research paper titled:

🎓 "Stress and Anxiety Prediction Using Machine Learning: A Benchmark Study of Key Algorithms"

📍 Presented at: IC2TMA – International Conference on Computational Techniques, Tools & Applications

📚 Published in: Panamerican Mathematical Journal (SCOPUS Indexed)

🧐 What’s the Research About?

With the world of today being so fast-paced, stress and anxiety are silent epidemics that have affected every single person from all generations. My research delves into the potential of machine learning (ML) to detect and predict such mental health disorders using real-world data.

We carried out a benchmark analysis of major ML algorithms to know which models provide the most precise prediction when it comes to detecting stress and anxiety levels based on behavioral and physiological patterns.

🧪 Methodology

In this research, we used a diverse dataset containing relevant features related to stress and anxiety. We applied several widely used machine learning algorithms, including:

🔹 Logistic Regression

🔹 Support Vector Machine (SVM)

🔹 Random Forest

🔹 K-Nearest Neighbors (KNN)

🔹 Gradient Boosting

All models were assessed using traditional performance measures such as accuracy, precision, recall, and F1-score to check how well it could predict stress and anxiety states.

📈 Key Findings

✅ The research reported that the ensemble learning models i.e., Random Forest and Gradient Boosting worked better than traditional classifiers in most scenarios.

✅Feature selection and feature engineering played a critical role in improving model accuracy.

✅ The research highlights the importance of AI in mental health screening and offers scope for developing early intervention systems.

🎤 Conference Presentation

It has been my privilege to present this paper before the distinguished IC2TMA International Conference, where it received constructive criticism and appreciation from researchers and professionals working under the fields of AI, mental health, and data science.

📰 Journal Publication

The paper is formally published in the Panamerican Mathematical Journal, a strongly ranked Scopus-indexed journal for the publication of high-impact computational and mathematical science scholarly research.

🙏 Acknowledgment

Without the advice of my instructors, support of my peers, reviewer and conference expert critiques, it could not have been done.
I also thank for the privilege to add my voice to the discourse on the relationship of technology with mental health.

📌 Final Thoughts

This work is a humble but significant step towards realizing the potential of machine learning to unravel and fight stress and anxiety—two of today's most relevant issues in modern society. I hope that this work inspires more students and researchers to learn how AI can benefit mental health.

Stay tuned for more updates, and don't hesitate to comment or ask questions below!

📅 Published on: 21 April 2025
✍️ Author: Mahesh Sananse