Subject(s)
Collagen Type I/blood , Diagnostic Tests, Routine/classification , Diagnostic Tests, Routine/economics , Peptides/blood , Reimbursement Mechanisms , Terminology as Topic , Biomarkers/blood , Blood Chemical Analysis/classification , Blood Chemical Analysis/economics , Blood Chemical Analysis/standards , Bone Resorption/blood , Bone Resorption/diagnosis , Collagen Type I/analysis , Diagnostic Tests, Routine/standards , France , Humans , Peptides/analysis , Reference Standards , Reimbursement Mechanisms/classificationABSTRACT
The Food and Drug Administration (FDA) is classifying hemoglobin A1c test system into class II (special controls). The special controls that will apply to this device are identified in this order and will be part of the codified language for the hemoglobin A1c test system classification. The Agency is classifying the device into class II (special controls) in order to provide a reasonable assurance of safety and effectiveness of the device.
Subject(s)
Blood Chemical Analysis/classification , Blood Chemical Analysis/instrumentation , Device Approval/legislation & jurisprudence , Glycated Hemoglobin/chemistry , Diabetes Mellitus/blood , Humans , United States , United States Food and Drug AdministrationABSTRACT
The objective of this study was determination and discrimination of biochemical data among three aquaculture-affected marine fish species (sea bass, Dicentrarchus labrax; sea bream, Sparus aurata L., and mullet, Mugil spp.) based on machine-learning methods. The approach relying on machine-learning methods gives more usable classification solutions and provides better insight into the collected data. So far, these new methods have been applied to the problem of discrimination of blood chemistry data with respect to season and feed of a single species. This is the first time these classification algorithms have been used as a framework for rapid differentiation among three fish species. Among the machine-learning methods used, decision trees provided the clearest model, which correctly classified 210 samples or 85.71%, and incorrectly classified 35 samples or 14.29% and clearly identified three investigated species from their biochemical traits.