Prediction of Decline in Global Cognitive Function Using Machine Learning with Feature Ranking of Gait and Physical Fitness Outcomes in Older Adults.

Byungjoo Noh,Hyemin Yoon,Sangjin Kim,Changhong Youm,Myeounggon Lee,Bohyun Kim,Hyejin Choi,Hwayoung Park,Yoonjae Noh

doi:10.3390/ijerph182111347

Abstract

Gait and physical fitness are related to cognitive function. A decrease in motor function and physical fitness can serve as an indicator of declining global cognitive function in older adults. This study aims to use machine learning (ML) to identify important features of gait and physical fitness to predict a decline in global cognitive function in older adults. A total of three hundred and six participants aged seventy-five years or older were included in the study, and their gait performance at various speeds and physical fitness were evaluated. Eight ML models were applied to data ranked by the p-value (LP) of linear regression and the importance gain (XI) of XGboost. Five optimal features were selected using elastic net on the LP data for men, and twenty optimal features were selected using support vector machine on the XI data for women. Thus, the important features for predicting a potential decline in global cognitive function in older adults were successfully identified herein. The proposed ML approach could inspire future studies on the early detection and prevention of cognitive function decline in older adults.

Highlights

Gait.IfIfthe thedata data were were randomly randomly assigned, assigned, the the perforperformance may deteriorate as the variables that do not have any effect would be inmance may deteriorate as the variables that do not have any effect would be included as cluded well
LP and XGBoost feature importance gain (XI) ranked data were used to select the important features that affect cognitive function scores based on the body information and gait data of 306 study participants
Twenty optimal features were determined from an support vector machine (SVM) of the XI ranked data

Summary

Introduction

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. Gait is the end product of the neuromusculoskeletal system, which executes a desired movement by using sensory inputs to modify motor patterns and muscular output [1]. The gait pattern is commonly generated by central pattern generators (CPGs), which are the source of tightly-coupled patterns of neural activity that drive rhythmic and stereotypical motor behavior such as locomotion [2]. The initiation and termination of movement, direction and speed change during walking, and obstacle avoidance require a supraspinal input to make movements that are adapted to the environment by modifying the basic gait pattern [2]

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