Predicting Metabolic Syndrome With Machine Learning Models Using a Decision Tree Algorithm: Retrospective Cohort Study

Metabolic syndrome is a cluster of disorders that significantly influence the development and deterioration of numerous diseases. FibroScan is an ultrasound device that was recently shown to predict metabolic syndrome with moderate accuracy. However, previous research regarding prediction of metabol...

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Veröffentlicht in:	JMIR medical informatics Jg. 8; H. 3; S. e17110
Hauptverfasser:	Yu, Cheng-Sheng, Lin, Yu-Jiun, Lin, Chang-Hsien, Wang, Sen-Te, Lin, Shiyng-Yu, Lin, Sanders H, Wu, Jenny L, Chang, Shy-Shin
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Canada JMIR Publications 01.03.2020
Schlagworte:	Algorithms Biopsy Blood pressure Cholesterol Classification Cohort analysis Data collection Decision trees Fasting Hemoglobin Hospitals Insulin resistance Kidney diseases Laboratories Liver Machine learning Metabolic syndrome Original Paper Overweight Triglycerides Ultrasonic imaging Womens health Taiwan metabolic syndrome decision tree controlled attenuation parameter technology machine learning
ISSN:	2291-9694, 2291-9694
Online-Zugang:	Volltext
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