Machine learning and explainable artificial intelligence to predict and interpret lead toxicity in pregnant women and unborn baby
We applied two widely used XAI methods, SHAP (SHapley additive explanations) and LIME (Local Interpretable Model-Agnostic Explanations), to provide insight into how each input feature contributed to the model's predictions.
Authors
- Sally McClean (ORCID: https://orcid.org/0000-0002-6871-3504)
- Priyanka Chaurasia (ORCID: https://orcid.org/0000-0003-4249-3678)
- Tabrez Jafar (ORCID: https://orcid.org/0000-0002-9461-5174)
- Abbas Ali Mahdi (ORCID: https://orcid.org/0000-0002-8580-9911)
- Pratheepan Yogarajah (ORCID: https://orcid.org/0000-0002-4586-7228)
- Sanjay Kumar Singh (ORCID: https://orcid.org/0000-0002-9061-6313)
- Mohammad Kaleem Ahmad (ORCID: https://orcid.org/0000-0001-6593-411X)
Institutions
- University of Ulster (GB)
- University of Lucknow (IN)
- King George's Medical University (IN)
- Indian Institute of Technology BHU (IN)
- Banaras Hindu University (IN)
Publication Details
- Journal
- Frontiers in Digital Health
- Published
- 2025-05-30
- DOI
- https://doi.org/10.3389/fdgth.2025.1608949
- Citations
- 4
- Primary Topic
- Heavy Metal Exposure and Toxicity
- Type
- article
- Field-Weighted Citation Impact
- 4.80