CLINICAL AND BIOLOGICAL PREDICTORS FOR COGNITIVE FRAILTY: A POPULATION PREDICTIVE MODEL

ABSTRACT: Abstract This study aims to create a population predictive model to gain a more in-depth understanding of the underlying biological mechanisms for cognitive frailty as currently defined by the International Consensus Group in 2013. Data were from the InCHIANTI study, collected at baseline from 1998–2000. This group is a representative sample (n=1,453) of a population of white European origin from two small towns in Tuscany, Italy. To build our model, we used biomarkers with implications for clinical research and practice; a total of 132 putative SNPs and 155 protein biomarkers were identified from a systematic review. We used a tree boosting model, Extreme Gradient Boosting (xgboost), a machine learning technique for supervised learning. We developed two predictive models with high accurac

SUBMITTER: Sargent L 

PROVIDER: S-EPMC6230026 | biostudies-literature | 2018 Nov

REPOSITORIES: biostudies-literature