Evidence map›Paper›PMID 42491591›Full record

ArticleiScience2026

Machine learning-driven prediction of heavy metal pollution for aquatic health surveillance and disease control.

Md Abdullah Al Mamun Hridoy, Pakorn Ditthakit, Akib Hosen, Mohd Saifur Rahman, Minhaj Uddin, Kamrul Hasan, Yeaman Hossain, Chiara Bordin, Leonardo Goliatt, Md Abdullah Al Mamun and 1 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in iScience, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors.

Md Abdullah Al Mamun HridoyFaculty of Fisheries, Sylhet Agricultural University, Sylhet 3100, Bangladesh.
Pakorn DitthakitCenter of Excellence in Sustainable Disaster Management, School of Engineering and Technology, Walailak University, Nakhon Si Thammarat 80161, Thailand.
Akib HosenDepartment of Environmental Science, Bangladesh Agricultural University, Mymensingh 2202, Bangladesh.
Mohd Saifur RahmanDepartment of Agricultural Construction and Environmental Engineering, Sylhet Agricultural University, Sylhet 3100, Bangladesh.
Minhaj UddinDepartment of Agricultural and Biological Engineering, Mississippi State University, 130 Creelman Street, Mississippi State, MS 39762, USA.
Kamrul HasanFaculty of Agricultural Economics and Business Studies, Sylhet Agricultural University, Sylhet 3100, Bangladesh.
Yeaman HossainDepartment of Agricultural Construction and Environmental Engineering, Sylhet Agricultural University, Sylhet 3100, Bangladesh.
Chiara BordinDepartment of Computer Science, UiT The Arctic University of Norway, 6050, Langnes, 9037 Tromsø, Norway.
Leonardo GoliattDepartment of Computational and Applied Mechanics, Federal University of Juiz de Fora, Juiz de Fora 36036-900, Brazil.
Md Abdullah Al MamunDepartment of Fish Health Management, Laboratory of Fish Diseases Diagnosis and Pharmacology, Faculty of Fisheries, Sylhet Agricultural University, Sylhet, Bangladesh.
Paolo PastorinoExperimental Zooprophylactic Institute of Piedmont, Liguria and Aosta Valley, Via Bologna 148, 10154 Turin, Italy.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Heavy metal (HM) accumulation in lake sediments poses ecological risks and may increase aquatic organism vulnerability to disease, requiring cost-effective monitoring tools. This study developed an interpretable multi-output machine learning (ML) framework to predict sediment HMs (As, Cd, Cu, Hg, Ni, Pb, and Zn) in Swan Lake, using field variables (pH, dissolved oxygen, and temperature), sediment texture (median grain size), and aluminum content (Al%). Models with nested preprocessing and cross-validation achieved strong performance (R

Indexed as

early warning systemsheavy metalmachine learningprediction modelwater quality

Identifiers

PMID42491591
PMCPMC13378350

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.