Preprocessing steps.

<div><p>In this study, we employed various machine learning models to predict metabolic phenotypes, focusing on thyroid function, using a dataset from the National Health and Nutrition Examination Survey (NHANES) from 2007 to 2012. Our analysis utilized laboratory parameters relevant to...

وصف كامل

محفوظ في:

التفاصيل البيبلوغرافية
المؤلف الرئيسي:	Hyeong Jun Ahn (7251695) (author)
مؤلفون آخرون:	Kyle Ishikawa (18933563) (author), Min-Hee Kim (295961) (author)
منشور في:	2024
الموضوعات:	Biochemistry Cancer Biological Sciences not elsewhere classified understanding complex relationships nutrition examination survey k nearest neighbors incorporating additional variables homeostatic model assessment gamma glutamyl transferase first two methods enhance model accuracy serum uric acid expanded variable sets predict metabolic phenotypes uncover potential associations metabolic phenotypes focusing metabolic phenotypes variable importance serum albumin metabolic dysregulation xlink "> upper ranks thyroxine ratio thyroid function specific requirements sampling weights sample weights results highlight respectively ). random forest potential benefits neural network national health machine learning insulin resistance health data future research followed closely demographic features data characteristics boosted trees 814 ). &# 8217
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Preprocessing steps.

مواد مشابهة