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• W4321372170 endingPage "798" @default.
• W4321372170 startingPage "798" @default.
• W4321372170 abstract "Hyperinsulinemia is a condition characterized by excessively high levels of insulin in the bloodstream. It can exist for many years without any symptomatology. The research presented in this paper was conducted from 2019 to 2022 in cooperation with a health center in Serbia as a large cross-sectional observational study of adolescents of both genders using datasets collected from the field. Previously used analytical approaches of integrated and relevant clinical, hematological, biochemical, and other variables could not identify potential risk factors for developing hyperinsulinemia. This paper aims to present several different models using machine learning (ML) algorithms such as naive Bayes, decision tree, and random forest and compare them with a new methodology constructed based on artificial neural networks using Taguchi's orthogonal vector plans (ANN-L), a special extraction of Latin squares. Furthermore, the experimental part of this study showed that ANN-L models achieved an accuracy of 99.5% with less than seven iterations performed. Furthermore, the study provides valuable insights into the share of each risk factor contributing to the occurrence of hyperinsulinemia in adolescents, which is crucial for more precise and straightforward medical diagnoses. Preventing the risk of hyperinsulinemia in this age group is crucial for the well-being of the adolescents and society as a whole." @default.
• W4321372170 created "2023-02-21" @default.
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• W4321372170 date "2023-02-20" @default.
• W4321372170 modified "2023-09-26" @default.
• W4321372170 title "Innovation in Hyperinsulinemia Diagnostics with ANN-L(atin square) Models" @default.
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• W4321372170 doi "https://doi.org/10.3390/diagnostics13040798" @default.
• W4321372170 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/36832286" @default.
• W4321372170 hasPublicationYear "2023" @default.
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