A machine learning-based data mining in medical examination data: a biological features-based biological age prediction model.
BMC Bioinformatics
; 23(1): 411, 2022 Oct 03.
Article
en En
| MEDLINE
| ID: mdl-36192681
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 ANDRESULTS:
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.Palabras clave
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Contexto en salud:
3_ND
Problema de salud:
3_neglected_diseases
Asunto principal:
Envejecimiento
/
Modelos Biológicos
Tipo de estudio:
Prognostic_studies
/
Risk_factors_studies
Aspecto:
Patient_preference
Límite:
Aged
/
Humans
/
Middle aged
Idioma:
En
Revista:
BMC Bioinformatics
Asunto de la revista:
INFORMATICA MEDICA
Año:
2022
Tipo del documento:
Article
País de afiliación:
China