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    A machine learning approach to dental fluorosis classification
    (Springer Science and Business Media Deutschland GmbH, 2021) Yetis, Aysegul Demir; Yesilnacar, Mehmet Irfan; Atas, Musa
    Fluoride in groundwater has been found to pose a severe public health threat in two villages (Karataş and Sarım) of western Sanliurfa in the southeastern Anatolia region of Turkey, where many cases of fluorosis, which detrimentally affects the teeth and bones, have been reported. Analysis of fluoride in drinking water is usually accomplished using various chemical methods, but while these techniques produce accurate and reliable results, they are expensive, labor-intensive, and cumbersome. In this study, a more cost-effective alternative, based on machine learning methods, is introduced. In this case, artificial neural network (ANN), support vector machine (SVM), and Naïve Bayes classifiers are utilized. Furthermore, a novel feature selection and ranking method known as Normalized Weighted Voting Map (NWVM) is presented. In Fisher discrimination power (FDP) scores, X-ray fluorescence (XRF) variables have higher discrimination power potential than X-Ray diffraction (XRD) attributes, the most salient feature being Zr (0.464) and CaO (219.993) from XRD and XRF, respectively. When the XRD and XRF parameters are classified separately for the effect of NWVM ranking scores on the fluoride values and dental fluoride in groundwater, CaO, SiO2, MgO, Fe2O3, P2O5, and K2O (for XRF) and Quartz and Zr (for XRD) present a stronger effect. In addition, when looking at the effects among themselves, the first order is the same XRF parameters and then the XRD parameters. Experiments revealed that XRF constituents including CaO, SiO2, MgO, P2O5, and K2O have higher class discrimination power than the XRD variables. © 2021, Saudi Society for Geosciences.
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    Öğe
    Novel machine learning techniques based hybrid models (LR-KNN-ANN and SVM) in prediction of dental fluorosis in groundwater
    (Springer, 2022) Atas, Musa; Yesilnacar, Mehmet Irfan; Yetis, Aysegul Demir
    Studies 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.