Feature Selection Algorithms Enhance the Accuracy of Frailty Indexes as Measures of Biological Age.

ABSTRACT: Biological age captures some of the variance in life expectancy for which chronological age is not accountable, and it quantifies the heterogeneity in the presentation of the aging phenotype in various individuals. Among the many quantitative measures of biological age, the mathematically uncomplicated frailty/deficit index is simply the proportion of the total health deficits in various health items surveyed in different individuals. We used 3 different statistical methods that are popular in machine learning to select 17-28 health items that together are highly predictive of survival/mortality, from independent study cohorts. From the selected sets, we calculated frailty indexes and Klemera-Doubal's biological age estimates, and then compared their mortality prediction performance using

SUBMITTER: Kim S 

PROVIDER: S-EPMC8277082 | biostudies-literature | 2021 Jul

REPOSITORIES: biostudies-literature