Novel machine learning techniques based hybrid models (LR-KNN-ANN and SVM) in prediction of dental fluorosis in groundwater

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dc.authoridDEMIR YETIS, AYSEGUL/0000-0003-4745-2445
dc.authoridAtas, Musa/0000-0002-9406-0076
dc.authoridYesilnacar, Mehmet Irfan/0000-0001-9724-8683
dc.contributor.authorAtas, Musa
dc.contributor.authorYesilnacar, Mehmet Irfan
dc.contributor.authorYetis, Aysegul Demir
dc.date.accessioned2024-12-24T19:24:36Z
dc.date.available2024-12-24T19:24:36Z
dc.date.issued2022
dc.departmentSiirt Üniversitesi
dc.description.abstractStudies have shown that excessive intake of fluoride into human body from drinking water may cause fluorosis adversely affects teeth and bones. Fluoride in water is mostly of geological origin and the amounts depend highly on many factors such as availability and solubility of fluoride minerals as well as hydrogeological and geochemical conditions. Chemical methods usually accomplish fluoride analysis in drinking water. The chemical methods are expensive, labor-intensive and time-consuming in general although accurate and reliable results are obtained. An alternative cost-effective approach based on machine learning (ML) technique is investigated in this study. Furthermore, most effective input parameters are selected via proposed Simulated Annealing (SA) search scheme. Selected subset (SAR, K+, NO3-, NO2-, Mn, Ba and Fe) by SA algorithm exhibited high correlation coefficient values of 0.731 and strong t test scores of 5.248. On the other hand, most frequently selected individual features were identified as NO3-, NO2-, Fe and SAR by vote map. The results of experiments revealed that selected feature subset improves the prediction performance of the learning models while feature size is reduced substantially. Thus it eventually enabled determination of fluoride in a cheap, fast and feasible way.
dc.description.sponsorshipTUBITAK (the Scientific and Technological Research Council of Turkey); Scientific Research Projects Committee of Harran University (HU BAK)
dc.description.sponsorshipTUBITAK (the Scientific and Technological Research Council of Turkey) and the Scientific Research Projects Committee of Harran University (HU BAK) provided financial support only for data collection and analysis.
dc.identifier.doi10.1007/s10653-021-01148-x
dc.identifier.endpage3905
dc.identifier.issn0269-4042
dc.identifier.issn1573-2983
dc.identifier.issue11
dc.identifier.pmid34739652
dc.identifier.scopus2-s2.0-85118618767
dc.identifier.scopusqualityQ1
dc.identifier.startpage3891
dc.identifier.urihttps://doi.org/10.1007/s10653-021-01148-x
dc.identifier.urihttps://hdl.handle.net/20.500.12604/6042
dc.identifier.volume44
dc.identifier.wosWOS:000714867300001
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.language.isoen
dc.publisherSpringer
dc.relation.ispartofEnvironmental Geochemistry and Health
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_20241222
dc.subjectDental fluorosis
dc.subjectDrinking water
dc.subjectMachine learning
dc.subjectFeature selection
dc.subjectSimulated annealing
dc.titleNovel machine learning techniques based hybrid models (LR-KNN-ANN and SVM) in prediction of dental fluorosis in groundwater
dc.typeArticle

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