Using a genetic algorithm to derive a highly predictive and context-specific frailty index
Alberto Zucchelli, Alessandra Marengoni, Debora Rizzuto, Amaia Calderón‐Larrañaga, Maurizio Zucchelli, Roberto Bernabei, Graziano Onder, Laura Fratiglioni, Davide Liborio Vetrano
Abstract
, to create a highly performant (FI) based on our prediction goals, rather than on a predetermined clinical selection of deficits, using data from the Swedish National Study on Aging and Care in Kungsholmen (SNAC-K) and 109 potential deficits identified in the dataset. The algorithm was personalized to obtain a FI with high discrimination ability in the prediction of mortality. The resulting FI included 40 deficits and showed areas under the curve consistently higher than 0.80 (range 0.81-0.90) in the prediction of 3-year and 6-year mortality in the whole sample and in sex and age subgroups. This methodology represents a promising opportunity to optimize the exploitation of medical and administrative databases in the construction of clinically relevant frailty indices.