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September 24, 2025PLoS ONEOpen Access

Prediction of biological age using machine learning

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This research demonstrates a machine learning approach that improves biological age prediction, suggesting health interventions.

The model improved predictive performance by 11.40% using LightGBM over XGBoost, validating its effectiveness.
MICE imputation for missing data enhanced prediction accuracy, resulting in a 23.35% increase in performance.
The Kaplan-Meier estimator confirmed that biological age effectively differentiates health levels among individuals.
Implementing the model's health recommendations could lead to a potential life expectancy increase of 64.58%.

Zhang et al. (2025) studied this question.

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