Predicting metabolic syndrome using machine learning – Analysis of commonly used indices

Elad Avizohar, Onn Shehory

Research output: Contribution to journalArticlepeer-review

Abstract

Determining the factors that contribute to making a reliable prediction of the metabolic syndrome will provide a deeper understanding of the medical indices involved in the prediction and assist in early diagnosis and treatment of patients. The study examined the optimal number of National cholesterol education program adult treatment panel (NCEP ATP) III indices needed to make a reliable prediction of the syndrome, whether each of the five NCEP ATP III indices for predicting the syndrome is equally important and whether a reliable prediction can be made using calculated blood pressure indices – estimated mean arterial pressure and pulse pressure – instead of NCEP ATP III blood pressure indices. The results show that NCEP ATP III indices for determination of the syndrome are not equally important. Moreover, the indices importance and their prediction quality vary according to gender. Optimal results are obtained by using all five NCEP ATP III indices for prediction.

Original languageEnglish
JournalHealth Informatics Journal
Volume29
Issue number4
DOIs
StatePublished - 1 Oct 2023

Bibliographical note

Publisher Copyright:
© The Author(s) 2023.

Keywords

  • gender
  • machine learning
  • metabolic syndrome
  • national cholesterol education program adult treatment panel III
  • prediction

Fingerprint

Dive into the research topics of 'Predicting metabolic syndrome using machine learning – Analysis of commonly used indices'. Together they form a unique fingerprint.

Cite this