https://github.com/junayed-hasan/occupational-stress-ml
This repository contains code, datasets, and analysis for AI-driven occupational stress detection using machine learning, deep learning, and NLP. It includes feature selection, explainable AI, synthetic data generation, and model validation for workplace safety applications. 🚀
https://github.com/junayed-hasan/occupational-stress-ml
anova cross-validation deployment-automation explainable-ai exploratory-data-analysis huggingface large-language-models machine-learning rfecv synthetic-dataset-generation
Last synced: 2 months ago
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This repository contains code, datasets, and analysis for AI-driven occupational stress detection using machine learning, deep learning, and NLP. It includes feature selection, explainable AI, synthetic data generation, and model validation for workplace safety applications. 🚀
- Host: GitHub
- URL: https://github.com/junayed-hasan/occupational-stress-ml
- Owner: junayed-hasan
- License: mit
- Created: 2024-11-27T12:46:28.000Z (6 months ago)
- Default Branch: main
- Last Pushed: 2025-03-09T07:35:28.000Z (2 months ago)
- Last Synced: 2025-03-09T08:19:25.311Z (2 months ago)
- Topics: anova, cross-validation, deployment-automation, explainable-ai, exploratory-data-analysis, huggingface, large-language-models, machine-learning, rfecv, synthetic-dataset-generation
- Language: Jupyter Notebook
- Homepage: https://huggingface.co/spaces/JnS123456/Occupational_stress_detection
- Size: 17.6 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# Early detection of occupational stress: Enhancing workplace safety with machine learning and large language models
**📌 A Machine Learning, Deep Learning, and NLP-based Approach**
[](LICENSE)
[](https://www.python.org/)
[](https://jupyter.org/)
[](#contributing)
---
## 📖 Table of Contents
1. [🔍 Overview](#-overview)
2. [📂 Repository Structure](#-repository-structure)
3. [💻 Tech Stack](#-tech-stack)
4. [🚀 Getting Started](#-getting-started)
5. [🔪 Usage](#-usage)
6. [📊 Results & Key Findings](#-results--key-findings)
7. [📌 Industry Relevance](#-industry-relevance)
8. [📝 License](#-license)
9. [🤝 Contributing](#-contributing)
10. [📬 Contact](#-contact)
---
## 🔍 Overview
**Workplace stress** is a critical issue that impacts **employee well-being, organizational efficiency, and workplace safety**. This repository provides **cutting-edge AI solutions** for detecting, analyzing, and mitigating occupational stress using a combination of:
✅ **Machine Learning (ML)** for stress detection models.
✅ **Deep Learning (DL)** using advanced neural networks.
✅ **Natural Language Processing (NLP)** to extract insights from survey data.
✅ **Explainable AI (XAI)** for interpretability in workplace safety decisions.
✅ **Synthetic Data Generation** to validate model generalizability.
**Why does this matter?**
- Stressed workers are **more prone to workplace accidents** and lower productivity.
- Organizations spend billions annually on absenteeism and medical costs due to work-related stress.
- Traditional stress assessments (e.g., surveys, self-reports) are slow, subjective, and lack predictive power.
**Our AI-driven approach** automates stress detection, enhances workplace safety, and enables proactive decision-making for both **organizations** and **policymakers**.
---
## 📂 Repository Structure
```
📦 Occupational-Stress-Safety
├── Main_results_holdout_val.ipynb # Main results reported in paper using holdout validation
├── explainable_ai/ # XAI-based analysis
│ ├── XAI_Occupational_Stress.ipynb
│
├── SOTA_analysis/ # State-of-the-art comparison
│ ├── SOTA_Paper_1.ipynb
│ ├── SOTA_Paper_2.ipynb
│ └── ...
│
├── domain_analysis/ # LLM-driven analysis
│ ├── LLM_anova_+_rfecv.ipynb
│
├── cross-validation/ # Model validation
│ ├── cross-validation.ipynb
│
├── anova/ # ANOVA-based feature selection
│ ├── anova.ipynb
│
├── rfecv/ # Recursive feature elimination
│ ├── RFECV.ipynb
│
├── synthetic_data_generation/ # Synthetic dataset generation
│ ├── Synthetic_data_generation.ipynb
│ ├── Synthetic_data_comparison.ipynb
│
├── eda/ # Exploratory Data Analysis
│ ├── EDA_Occupational_Stress.ipynb
│
├── ablations/ # Impact of feature selection
│ ├── Ablation_no_RFECV.ipynb
│ ├── Ablation_no_anova.ipynb
│ ├── Ablation_no_zero_var.ipynb
│
├── dataset/ # Raw dataset
│ ├── DIB dataset and codebook.xlsx
│
├── synthetic_dataset/ # Generated datasets
│ ├── synthetic_data_tvae.csv
│ ├── synthetic_data_gaussian_copula.csv
│ ├── synthetic_data_ctgan.csv
│ ├── synthetic_data_copulaGan.csv
│
├── LICENSE # MIT License
└── README.md # This file
```
---
## 💻 Tech Stack
| Technology | Usage |
|-----------------|------------------------------------------------|
| **Python** | Core programming language |
| **Jupyter** | Interactive computing environment |
| **Pandas** | Data manipulation |
| **NumPy** | Numerical computing |
| **Scikit-learn**| Machine learning models & evaluation |
| **TensorFlow** | Deep learning framework |
| **Hugging Face Transformers** | NLP and Large Language Models |
| **Seaborn & Matplotlib** | Data visualization |
| **SHAP & LIME** | Explainable AI (XAI) |
---
## 🚀 Getting Started
1. **Clone the Repository**
```bash
git clone https://github.com/junayed-hasan/occupational-stress-ml.git
cd Occupational-Stress-Safety
```
2. **Create a Virtual Environment**
```bash
python -m venv venv
source venv/bin/activate # Mac/Linux
.\venv\Scripts\activate # Windows
```
3. **Install Dependencies**
```bash
pip install -r requirements.txt
```
4. **Run Jupyter Notebooks**
```bash
jupyter notebook
```
---
## 🔪 Usage
### **1️⃣ Data Preprocessing & EDA**
- Run `eda/EDA_Occupational_Stress.ipynb` to explore dataset characteristics.
### **2️⃣ Feature Selection & Model Training**
- `anova/anova.ipynb` for ANOVA-based feature selection.
- `rfecv/RFECV.ipynb` for recursive feature elimination.
- `cross-validation/cross-validation.ipynb` to validate models.
### **3️⃣ Synthetic Data Generation**
- Run `synthetic_data_generation/Synthetic_data_generation.ipynb` to generate synthetic datasets.
### **4️⃣ Explainability & Domain Analysis**
- `explainable_ai/XAI_Occupational_Stress.ipynb` provides model interpretability.
- `domain_analysis/LLM_anova_+_rfecv.ipynb` utilizes NLP models for deeper insights.
---
## 📊 Results & Key Findings
- **📌 Achieved 90.32% Accuracy** in occupational stress classification.
- **📌 Top predictors of stress:** Workload, Manager Support, Job Role Clarity.
- **📌 LLMs outperformed traditional NLP models in stress classification tasks.**
- **📌 Synthetic data generation proved effective, achieving ~89% accuracy on unseen test scenarios.**
For full details, check:
- `Main_results_holdout_val.ipynb` (final results)
- `ablations/` (impact of feature selection methods)
---
## 📌 Industry Relevance
This research has **direct implications** for:
✔ **HR & Workforce Management** – Predict stress & reduce workplace accidents.
✔ **Occupational Health & Safety Teams** – Implement AI-driven monitoring.
✔ **Government & Policymakers** – Set data-driven workplace safety regulations.
✔ **AI & Data Science Practitioners** – Develop real-world stress detection models.
---
## 📝 License
This project is licensed under the [MIT License](LICENSE), allowing free use, modification, and distribution.
---
## 🤝 Contributing
Contributions are welcome! To contribute:
1. Fork the repository.
2. Create a new branch (`feature-new-feature`).
3. Commit changes and open a pull request.
---
## 📬 Contact
📌 **Collaborators:** Mohammad Junayed Hasan and Jannat Sultana
📌 **Supervisors:** Dr. Sifat Momen ([email protected], https://scholar.google.com/citations?user=sGVZEaAAAAAJ), Ms. Silvia Ahmed ([email protected], https://scholar.google.com/citations?user=T5jK--YAAAAJ&hl=en&oi=ao)
📌 **Emails:** [email protected], [email protected]
📌 **LinkedIn:** https://www.linkedin.com/in/mjhasan21/, https://www.linkedin.com/in/jannat-sultana/
We appreciate your interest in our research and welcome discussions, collaborations, and feedback! 🚀