1. A machine learning-based data mining in medical examination data: a biological features-based biological age prediction model.
- Author
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Yang, Qing, Gao, Sunan, Lin, Junfen, Lyu, Ke, Wu, Zexu, Chen, Yuhao, Qiu, Yinwei, Zhao, Yanrong, Wang, Wei, Lin, Tianxiang, Pan, Huiyun, and Chen, Ming
- Subjects
PERIODIC health examinations ,MACHINE learning ,DATA mining ,AGE ,PREDICTION models ,CHINESE people - Abstract
Background: Biological age (BA) has been recognized as a more accurate indicator of aging than chronological age (CA). However, the current limitations include: insufficient attention to the incompleteness of medical data for constructing BA; Lack of machine learning-based BA (ML-BA) on the Chinese population; Neglect of the influence of model overfitting degree on the stability of the association results. Methods and results: Based on the medical examination data of the Chinese population (45–90 years), we first evaluated the most suitable missing interpolation method, then constructed 14 ML-BAs based on biomarkers, and finally explored the associations between ML-BAs and health statuses (healthy risk indicators and disease). We found that round-robin linear regression interpolation performed best, while AutoEncoder showed the highest interpolation stability. We further illustrated the potential overfitting problem in ML-BAs, which affected the stability of ML-Bas' associations with health statuses. We then proposed a composite ML-BA based on the Stacking method with a simple meta-model (STK-BA), which overcame the overfitting problem, and associated more strongly with CA (r = 0.66, P < 0.001), healthy risk indicators, disease counts, and six types of disease. Conclusion: We provided an improved aging measurement method for middle-aged and elderly groups in China, which can more stably capture aging characteristics other than CA, supporting the emerging application potential of machine learning in aging research. [ABSTRACT FROM AUTHOR]
- Published
- 2022
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