Mental Stress Detection Using Machine Learning

@article{1707576,
      author = {Dinesh A, Bhalanath Mohanta, Byrava M, Humaun Forhat},
      title = {Mental Stress Detection Using Machine Learning},
      journal = {Iconic Research And Engineering Journals},
      year = {2025},
      volume = {8},
      number = {9},
      pages = {1042-1045},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1707576.pdf},
      abstract = {This paper pivots on detecting mental stress levels among employees and students using a machine learning model called the Random Forest Classifier. A dataset from Kaggle, based on employees and students and emotional responses to various questions, was used to calculate stress scores. The model is focused to achieve 100% training accuracy and 95% test accuracy, proving its reliability. A web application was developed using Flask, where user answer the questions, and the system predicts their stress levels. This non-invasive tool can help identify high-stress individuals early, enabling timely support and promoting mental health. This project aims to foster well-being in the society through technology-driven solutions. By leveraging machine learning, it promotes mental health support within society, fostering a health.},
      keywords = {Machine Learning, Mental Stress Detection, Random Forest Classifier.},
      month = {March},
  }