{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,20]],"date-time":"2026-08-20T18:24:54Z","timestamp":1787250294703,"version":"build-2736575974"},"reference-count":74,"publisher":"Public Library of Science (PLoS)","issue":"8","license":[{"start":{"date-parts":[[2026,8,20]],"date-time":"2026-08-20T00:00:00Z","timestamp":1787184000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS One"],"abstract":"<jats:p>\n                    <jats:italic>Escherichia coli (E. coli)<\/jats:italic>\n                    is a key indicator of fecal contamination in freshwater and can signal the presence of other harmful bacteria and viruses. The aim of the study is to evaluate the performance of machine learning (ML) tools to detect\n                    <jats:italic>E. coli<\/jats:italic>\n                    in drinking water in Bangladesh using surveillance data under two scenarios: an imbalanced dataset and a balanced dataset. We utilized data from the 2019 Bangladesh Multiple Indicator Cluster Survey, which included a total of 6,069 household drinking water samples. We used agglomerative hierarchical clustering with Ward\u2019s linkage to identify district-level hotspots. Extreme Gradient Boosting with SHapley Additive exPlanations values were used for feature selection, and the Synthetic Minority Over-sampling Technique (SMOTE) was used to address class imbalance in the classification task. We applied nine classical ML models in this study: Adaptive Boosting (AdaBoost), Decision Trees (DT), Gradient Boosting Algorithm (GBA), k-Nearest Neighbors (KNN), Light Gradient-Boosting Machine (LightGBM), Logistic Regression (LR), Na\u00efve Bayes (NB), Random Forest (RF), and Support Vector Machine (SVM), along with a Deep Learning Multi-Layer Perceptron (DL-MLP) model to predict the risk of\n                    <jats:italic>E. coli<\/jats:italic>\n                    contamination (REcC) in water. Model performance was evaluated using accuracy, precision, recall, F1 score, Cohen Kappa, area under the curve (AUC), and a violin plot.\n                    <jats:italic>E. coli<\/jats:italic>\n                    contamination in drinking water was detected in 39.2% (95% CI: 37.4\u201341.2) of households. Bandarban district had the highest REcC. After applying SMOTE and 10-fold cross-validation with hyperparameter tuning, model performance was more consistent across algorithms. In terms of model evaluation, AdaBoost slightly outperformed the others with an accuracy of 81.6%, Cohen kappa statistic of 19.4%, precision of 82.2%, recall of 99%, F1-score of 89.8%, and an AUC of 68.6%. Ensemble model for example AdaBoost and GBA models had ability to accurately classify drinking water samples with respect to the presence of\n                    <jats:italic>E. coli<\/jats:italic>\n                    using surveillance data than others selected model in this study.\n                  <\/jats:p>","DOI":"10.1371\/journal.pone.0343606","type":"journal-article","created":{"date-parts":[[2026,8,20]],"date-time":"2026-08-20T17:41:16Z","timestamp":1787247676000},"page":"e0343606","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":0,"title":["Use of machine learning to detect Escherichia coli in drinking water in Bangladesh"],"prefix":"10.1371","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9183-120X","authenticated-orcid":true,"given":"Iqramul","family":"Haq","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Md. Yusuf Hossain","family":"Ador","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9821-1436","authenticated-orcid":true,"given":"Diego","family":"Nobrega","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2026,8,20]]},"reference":[{"issue":"12","key":"pone.0343606.ref001","doi-asserted-by":"crossref","first-page":"6235","DOI":"10.3390\/ijerph10126235","article-title":"Escherichia coli in Europe: An overview","volume":"10","author":"N Allocati","year":"2013","journal-title":"Int J Environ Res Public Health"},{"issue":"3","key":"pone.0343606.ref002","doi-asserted-by":"crossref","first-page":"268","DOI":"10.1080\/09603123.2019.1593328","article-title":"Spatial risk distribution and determinants of E. coli contamination in household drinking water: A case study of Bangladesh","volume":"30","author":"JR Khan","year":"2020","journal-title":"Int J Environ Health Res"},{"key":"pone.0343606.ref003","doi-asserted-by":"crossref","first-page":"117089","DOI":"10.1016\/j.watres.2021.117089","article-title":"Exploration of E. coli contamination drivers in private drinking water wells: An application of machine learning to a large, multivariable, geo-spatio-temporal dataset","volume":"197","author":"K White","year":"2021","journal-title":"Water Res"},{"issue":"6","key":"pone.0343606.ref004","doi-asserted-by":"crossref","first-page":"856","DOI":"10.1289\/ehp.9430","article-title":"Massive microbiological groundwater contamination associated with a waterborne outbreak in Lake Erie, South Bass Island, Ohio","volume":"115","author":"T-T Fong","year":"2007","journal-title":"Environ Health Perspect"},{"issue":"5","key":"pone.0343606.ref005","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0093301","article-title":"Contamination of groundwater systems in the US and Canada by enteric pathogens, 1990\u20132013: A review and pooled-analysis","volume":"9","author":"PD Hynds","year":"2014","journal-title":"PLoS One"},{"key":"pone.0343606.ref006","doi-asserted-by":"crossref","first-page":"329","DOI":"10.1016\/j.envpol.2018.02.052","article-title":"Development of a hierarchical model for predicting microbiological contamination of private groundwater supplies in a geologically heterogeneous region","volume":"237","author":"J O\u2019Dwyer","year":"2018","journal-title":"Environ Pollut"},{"issue":"4","key":"pone.0343606.ref007","first-page":"280","article-title":"Keeping clean water clean in a Malawi refugee camp: A randomized intervention trial","volume":"79","author":"L Roberts","year":"2001","journal-title":"Bull World Health Organ"},{"issue":"3","key":"pone.0343606.ref008","doi-asserted-by":"crossref","first-page":"177","DOI":"10.1016\/j.actatropica.2013.02.023","article-title":"Relationship between Aedes aegypti production and occurrence of Escherichia coli in domestic water storage containers in rural and sub-urban villages in Thailand and Laos","volume":"126","author":"N Dada","year":"2013","journal-title":"Acta Trop"},{"issue":"5","key":"pone.0343606.ref009","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pmed.1001644","article-title":"Fecal contamination of drinking-water in low- and middle-income countries: A systematic review and meta-analysis","volume":"11","author":"R Bain","year":"2014","journal-title":"PLoS Med"},{"issue":"7","key":"pone.0343606.ref010","doi-asserted-by":"crossref","first-page":"505","DOI":"10.1080\/15592294.2017.1329068","article-title":"Machine learning for epigenetics and future medical applications","volume":"12","author":"LB Holder","year":"2017","journal-title":"Epigenetics"},{"issue":"1","key":"pone.0343606.ref011","doi-asserted-by":"crossref","first-page":"575","DOI":"10.1186\/s12859-021-04491-z","article-title":"Predicting environmentally responsive transgenerational differential DNA methylated regions (epimutations) in the genome using a hybrid deep-machine learning approach","volume":"22","author":"P Mavaie","year":"2021","journal-title":"BMC Bioinformatics"},{"key":"pone.0343606.ref012","doi-asserted-by":"crossref","unstructured":"Deng J, Dong W, Socher R, Li L-J, Kai Li, Li Fei-Fei. 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