ArticleJournal of clinical laboratory analysis2025
Age Estimation From Blood Test Results Using a Random Forest Model.
Article in Journal of clinical laboratory analysis, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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6 authors.
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Abstract
BACKGROUND AND
objectivesFrom a preventive medicine perspective, this study aims to clarify the role of screening data in aging and health problems by estimating age from screening data and verifying the number of data items required in widely used screening tests. MATERIALS AND
methodsA random forest model was applied to 11554 men and women (3043 and 8511, respectively) aged 0-95 years who underwent screening tests (60 blood tests, 8 urine tests and 2 saliva tests) between February 2020 and August 2023. All analyses were conducted in Python 3.10.12.
resultsUsing all 71 items including gender, a high accuracy of R
conclusionsAge estimation from blood data using the random forest model (blood age) is sufficiently precise for assessing physical aging state. Blood age, as well as other biological ages estimated from various omics estimators, was shown to be a very promising method for exploring the problems of aging such as metabolic syndrome and frail syndrome.
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