Version 1
: Received: 29 October 2023 / Approved: 30 October 2023 / Online: 30 October 2023 (10:09:26 CET)
How to cite:
Panda, K.; Mazumder, A. Leveraging Machine Learning for a Comprehensive Assessment of PFAS Nephrotoxicity. Preprints2023, 2023101855. https://doi.org/10.20944/preprints202310.1855.v1
Panda, K.; Mazumder, A. Leveraging Machine Learning for a Comprehensive Assessment of PFAS Nephrotoxicity. Preprints 2023, 2023101855. https://doi.org/10.20944/preprints202310.1855.v1
Panda, K.; Mazumder, A. Leveraging Machine Learning for a Comprehensive Assessment of PFAS Nephrotoxicity. Preprints2023, 2023101855. https://doi.org/10.20944/preprints202310.1855.v1
APA Style
Panda, K., & Mazumder, A. (2023). Leveraging Machine Learning for a Comprehensive Assessment of PFAS Nephrotoxicity. Preprints. https://doi.org/10.20944/preprints202310.1855.v1
Chicago/Turabian Style
Panda, K. and Anirudh Mazumder. 2023 "Leveraging Machine Learning for a Comprehensive Assessment of PFAS Nephrotoxicity" Preprints. https://doi.org/10.20944/preprints202310.1855.v1
Abstract
Polyfluoroalkyl substances (PFAS) are persistent chemicals that accumulate in the body and environment. Although recent studies have indicated that PFAS may disrupt kidney function, the underlying mechanisms and overall effects on the organ remain unclear. Therefore, this study aims to elucidate the impact of PFAS on kidney health using machine learning techniques. Utilizing a dataset containing PFAS chemical features and kidney parameters, dimensionality reduction and clustering were performed to identify patterns. Machine learning models, including XGBoost classifier, regressor, and Random Forest regressor, were then developed to predict kidney type from PFAS descriptors, estimate PFAS accumulation in the body, and predict the ratio of glomerular surface area to proximal tubule volume, which indicates kidney filtration efficiency. The kidney type classifier achieved 100% accuracy, confirming that PFAS exposure alters kidney morphology. The PFAS accumulation model attained an R^2 of 1.00, providing a tool to identify at-risk individuals. The ratio prediction model reached an R^2 of 1.00, offering insights into PFAS effects on kidney function. Furthermore, PFAS descriptors and anatomical variables were identified through analyses using feature importance, demonstrating discernible links between PFAS and kidney health, offering further biological significance. Overall, this study can significantly contribute to the current findings on the effect of PFAS while offering machine learning as a contributive tool for similar studies.
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
