MACHINE LEARNING APPROACH TO CLASSIFY DECLINE OF COGNITIVE AND MUSCLE FUNCTION IN OLDER WOMEN: GAIT CHARACTERISTICS BASED ON THREE SPEEDS

Authors

Bohyun Kim, Dong-A UniversityFollow
Changhong Youm, Dong-A UniversityFollow
Hwayoung Park, Dong-A UnivercityFollow
Hyejin Choi, Dong-A UnivercityFollow
Juseon Hwang, Dong-A UnivercityFollow
Minsoo Kim, Dong-A UnivercityFollow

Category

Growth and aging

Document Type

Paper

Abstract

This study aimed to evaluate the accuracy of machine learning models based on selected gait features caused by cognitive (Cog) and muscle function (MF) declines. A total of 154 women aged 65 or older performed cognitive assessments, five times sit-to-stand test, and gait test at three speeds (preferred, slower (SWS), and faster walking speed (FWS)). The machine learning model accuracies revealed that the random forest (RF) model had 91.2% accuracy when using all gait features and 91.9% accuracy when using the three features (walking speed and coefficient of variation of the left double support phase at FWS and right double support phase at SWS) selected for Cog+MF+ and Cog–MF– classification. We suggest that machine learning analysis using selected gait features may help improve the objective classification and evaluation of Cog and MF in older women.

Recommended Citation

Kim, Bohyun; Youm, Changhong; Park, Hwayoung; Choi, Hyejin; Hwang, Juseon; and Kim, Minsoo (2024) "MACHINE LEARNING APPROACH TO CLASSIFY DECLINE OF COGNITIVE AND MUSCLE FUNCTION IN OLDER WOMEN: GAIT CHARACTERISTICS BASED ON THREE SPEEDS," ISBS Proceedings Archive: Vol. 42: Iss. 1, Article 37.
Available at: https://commons.nmu.edu/isbs/vol42/iss1/37