A machine learning-based data mining in medical examination data: a biological features-based biological age prediction model

Abstract

Background: Biological age (BA) has been recognized as a more accurate indicator of aging than chronological age (CA). However, current limitations include insufficient attention to the incompleteness of medical data used to construct BA; lack of machine learning-based BA (ML-BA) on the Chinese population; neglect of the influence of the degree of model overfitting on the stability of the correlation 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 AE showed the highest stability at high missing rates. 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.