Explainable Machine Learning Prediction of PCB-138 Behavior Patterns in Edible Fish from Croatian Adriatic
Explainable Machine Learning Prediction of PCB-138 Behavior Patterns in Edible Fish from Croatian Adriatic
Autori:
Izdanje: International Scientific Conference on Information Technology and Data Related Research
DOI: 10.15308/Sinteza-2020-23-28
Oblast: Artificial Intelligence Atlas
Stranice: 23-28
Apstrakt:
Fish consumption, especially consumption of oily marine species, is globally
increasing since it has been recommended by dieticians due to high content
of polyunsaturated ω-3 and ω-6 (PUFAs) fatty acids in fish tissue. Health
benefits of PUFA ingestion coincide with the risk of intake of hazardous
lipophilic persistent pollutants including organochlorine pesticides (OCPs)
and related polychlorinated biphenyls (PCBs). We examined the impacts of
18 fatty acids (FAs) and 36 toxic organic and inorganic contaminants on the
behavior patterns of indicator congener PCB-138 in marine fish using eXtreme
Gradient Boosting (XGBoost), SHapley Additive exPlanations (SHAP), and
SHAP value fuzzy clustering. XGBoost indicated non-linear relationships
between investigated variables that surpasses indications suggested by commonly
applied correlation matrices. Ten extracted fuzzy clusters of SHAP
values revealed that higher intake of saturated myristic-C14:0 and margaric-
C17:0 acids followed by intake of nutritionally beneficial eicosadienoic acid
(C20:2n-6) mostly contributed to the PCB-138 bioaccumulation. Important
impacts on PCB-138 behavior patterns were also registered for chemically
allied indicator congeners (-153 and -180) and organochlorines’ metabolite
p,p’-DDE. Less prominent were the associations between target congener
and the most toxic dioxin-like PCBs.
increasing since it has been recommended by dieticians due to high content
of polyunsaturated ω-3 and ω-6 (PUFAs) fatty acids in fish tissue. Health
benefits of PUFA ingestion coincide with the risk of intake of hazardous
lipophilic persistent pollutants including organochlorine pesticides (OCPs)
and related polychlorinated biphenyls (PCBs). We examined the impacts of
18 fatty acids (FAs) and 36 toxic organic and inorganic contaminants on the
behavior patterns of indicator congener PCB-138 in marine fish using eXtreme
Gradient Boosting (XGBoost), SHapley Additive exPlanations (SHAP), and
SHAP value fuzzy clustering. XGBoost indicated non-linear relationships
between investigated variables that surpasses indications suggested by commonly
applied correlation matrices. Ten extracted fuzzy clusters of SHAP
values revealed that higher intake of saturated myristic-C14:0 and margaric-
C17:0 acids followed by intake of nutritionally beneficial eicosadienoic acid
(C20:2n-6) mostly contributed to the PCB-138 bioaccumulation. Important
impacts on PCB-138 behavior patterns were also registered for chemically
allied indicator congeners (-153 and -180) and organochlorines’ metabolite
p,p’-DDE. Less prominent were the associations between target congener
and the most toxic dioxin-like PCBs.
Ključne reči: persistent organic pollutants (POPs), (omega-3-6) fatty acids, heavy metals, Shapley Additive exPlanations (SHAP), fuzzy methods
Priložene datoteke:
- 23-28 ( veličina: 821,49 KB, broj pregleda: 277 )
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BibTeX format
@article{article,
author = {A. Stojić, B. Mustać and G. Jovanović},
title = {Explainable Machine Learning Prediction of PCB-138 Behavior Patterns in Edible Fish from Croatian Adriatic},
journal = {International Scientific Conference on Information Technology and Data Related Research},
year = 2020,
pages = {23-28},
doi = {10.15308/Sinteza-2020-23-28}
}
RT Conference Proceedings A1 Andreja Stojić A1 Bosiljka Mustać A1 Gordana Jovanović T1 Explainable Machine Learning Prediction of PCB-138 Behavior Patterns in Edible Fish from Croatian Adriatic AD Univerzitet Singidunum, Beograd, Beograd, Srbija YR 2020 NO doi: 10.15308/Sinteza-2020-23-28
A. Stojić, B. Mustać and G. Jovanović, Explainable Machine Learning Prediction of PCB-138 Behavior Patterns in Edible Fish from Croatian Adriatic, Univerzitet Singidunum, Beograd, 2020, doi:10.15308/Sinteza-2020-23-28