Deviation of Physiological from Chronological Age Is Associated with Health

Lin Peretz, Nadav Rappoport

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Scopus citations


Biological age may be of higher importance than chronological age, yet biological age is not trivial to estimate. This study presents a regression model to predict age using routine clinical tests like laboratory tests using the UK Biobank (UKBB) data. We run different machine learning regression models for this predictions task and compare their performance according to RMSE. The models were trained using data from 472,189 subjects aged 37-82 years old and 61 different laboratory tests results. Our chosen model was an XGboost model, which achieved an RMSE of 6.67 years. Subjects whose the model predicted to be younger than their actual age were found to be healthier as they had fewer diagnoses, fewer operations, and had a lower prevalence of specific diseases than age-matched controls. On the other hand, subjects predicted to be older than their chronological age had no significant differences in the number of diagnoses, number of operations, and specific diseases than age-matched controls.

Original languageEnglish
Title of host publicationChallenges of Trustable AI and Added-Value on Health - Proceedings of MIE 2022
EditorsBrigitte Seroussi, Patrick Weber, Ferdinand Dhombres, Cyril Grouin, Jan-David Liebe, Jan-David Liebe, Jan-David Liebe, Sylvia Pelayo, Andrea Pinna, Bastien Rance, Bastien Rance, Lucia Sacchi, Adrien Ugon, Adrien Ugon, Arriel Benis, Parisis Gallos
PublisherIOS Press BV
Number of pages5
ISBN (Electronic)9781643682846
StatePublished - 2022
Event32nd Medical Informatics Europe Conference, MIE 2022 - Nice, France
Duration: 27 May 202230 May 2022

Publication series

NameStudies in Health Technology and Informatics
ISSN (Print)0926-9630
ISSN (Electronic)1879-8365


Conference32nd Medical Informatics Europe Conference, MIE 2022


  • BioBank
  • Biological age
  • Chronological age
  • Electronic Health Records
  • Laboratory tests
  • Machine Learning

ASJC Scopus subject areas

  • Biomedical Engineering
  • Health Informatics
  • Health Information Management


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