A Machine Learning Approach to Predict Biological Age and Its Longitudinal Drivers
Dunbayeva, Nazira Li, Yulong Xie, Yutong Razzak, Imran
Dunbayeva, Nazira
Li, Yulong
Xie, Yutong
Razzak, Imran
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Computer Vision
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Conference proceeding
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Abstract
Predicting an individual’s aging trajectory is a central challenge in preventative medicine and bioinformatics. While machine learning models can predict chronological age from biomarkers, they often fail to capture the dynamic, longitudinal nature of the aging process. In this work, we developed and validated a machine learning pipeline to predict age using a longitudinal cohort with data from two distinct time periods (2019-2020 and 2021-2022). We demonstrate that a model using only static, cross-sectional biomarkers has limited predictive power when generalizing to future time points. However, by engineering novel features that explicitly capture the rate of change (slope) of key biomarkers over time, we significantly improved model performance. Our final LightGBM model, trained on the initial wave of data, successfully predicted age in the subsequent wave with high accuracy (R 2 = 0.515 for males, R 2 = 0.498 for females). SHAP analysis revealed that the engineered slope features were among the most impactful predictors, highlighting that an individual’s health trajectory, not simply their static health snapshot, drives biological age. This framework enables real-time tracking and early intervention for age-related risks.
Citation
N. Dunbayeva, Y. Li, Y. Xie, I. Razzak, "A Machine Learning Approach to Predict Biological Age and Its Longitudinal Drivers," 2026, pp. 517-526.
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Proceedings of the 19th International Joint Conference on Biomedical Engineering Systems and Technologies
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Proceedings of the 19th International Joint Conference on Biomedical Engineering Systems and Technologies
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INSTICC