{
  "abstract": "Occupational burnout can develop slowly, making it difficult to detect in the early stages. The primary data for assessing the situation come periodically from the person experiencing it. The paper discusses the potential to detect occupational burnout by integrating biological, psychological, and work-related data through statistical analysis and machine learning. The research outlines biological sensors, physiological stress responses, and machine-learning-based prediction of mental pressure, focusing on heart rate variability, electrodermal activity, sleep, and physical activity. The study suggests using descriptive statistics and correlation analysis to assess relationships between major variables, and logistic regression as a simple statistical prediction method. Two machine learning models can be readily employed for pattern identification of early burnout risk: a Decision Tree and k-nearest neighbors (k-NN). The sensitivity, specificity, and the area under the receiver operating characteristic curve (AUC) can be used to assess model performance. The main research question is whether biological information predicts outcomes beyond psychological and occupational information. The article addresses the issue through the MEBTF, which can overcome the problem by using multimodal measurement methods to detect changes in risk trajectories rather than diagnosing burnout. The research shows considerable potential; however, it has identified biological outcomes, a lack of external validation, and a small sample size. Future prospective research must address these issues.\n\nThis is a preprint / defensive disclosure to establish priority. This version is not peer-reviewed. An extended version will be submitted to a peer-reviewed journal",
  "abstract_shown": true,
  "affiliations": [
    "University of Agriculture Faisalabad",
    "South East Technological University, Ireland",
    "Bahauddin Zakariya University"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 3,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Nayab Kanwal"
    },
    {
      "affs": [
        1
      ],
      "name": "Tabassum Dr Mujahid"
    },
    {
      "affs": [
        2
      ],
      "name": "Hammad Muhammad"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/78zn6mp6v3dg2.bib",
  "cited_by": 0,
  "doi": "10.5281/zenodo.23571582",
  "fulltext_url": null,
  "id": "78zn6mp6v3dg2",
  "issns": [],
  "landing_url": "https://zenodo.org/records/23571582",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [],
  "openalex": null,
  "pdf_url": "https://zenodo.org/api/records/23571582/files/Article-ML-Bio.pdf/content",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-22",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/78zn6mp6v3dg2.ris",
  "sources": [
    {
      "first_seen": "2026-11-05T12:20:24Z",
      "native_id": "23571582",
      "source": "zenodo",
      "url": "https://zenodo.org/records/23571582"
    }
  ],
  "subjects": [],
  "title": "Integrating Machine Learning and Biological Stress Indicators for Early Prediction of Occupational Burnout among Young Professionals",
  "title_text": "Integrating Machine Learning and Biological Stress Indicators for Early Prediction of Occupational Burnout among Young Professionals",
  "type": "preprint",
  "updated_at": "2026-11-05T12:20:24Z",
  "url": "https://research.draconitic.com/paper/78zn6mp6v3dg2",
  "venue": "Zenodo",
  "versions": []
}