Development of an effective clustering algorithm for older fallers
Document Type
Article
Publication Date
11-1-2022
Abstract
Falls are common and often lead to serious physical and psychological consequences for older persons. The occurrence of falls are usually attributed to the interaction between multiple risk factors. The clinical evaluation of falls risks is time-consuming as a result, hence limiting its availability. The purpose of this study was, therefore, to develop a clustering-based algorithm to determine falls risk. Data from the Malaysian Elders Longitudinal Research (MELoR), comprising 1411 subjects aged >= 55 years, were utilized. The proposed algorithm was developed through the stages of: data pre-processing, feature identification and extraction with either t-Distributed Stochastic Neighbour Embedding (t-SNE) or principal component analysis (PCA)), clustering (K-means clustering, Hierarchical clustering, and Fuzzy C-means clustering) and characteristics interpretation with statistical analysis. A total of 1279 subjects and 9 variables were selected for clustering after the data pre-possessing stage. Using feature extraction with the t-SNE and the K-means clustering algorithm, subjects were clustered into low, intermediate A, intermediate B and high fall risk groups which corresponded with fall occurrence of 13%, 19%, 21% and 31% respectively. Slower gait, poorer balance, weaker muscle strength, presence of cardiovascular disorder, poorer cognitive performance, and advancing age were the key variables identified. The proposed fall risk clustering algorithm grouped the subjects according to features. Such a tool could serve as a case identification or clinical decision support tool for clinical practice to enhance access to falls prevention efforts.
Keywords
Feature-Selection, Risk, Prevention, People, Gender
Divisions
biomedengine,medicinedept
Funders
Ministry of Education, Malaysia UM.C/625/1/HIR/MOHE/ARTS/02,UTAR Research Fund from the Universiti Tunku Abdul Rahman (UTAR) IPSR/RMC/UTARRF/2020-C1/G01
Publication Title
PLoS ONE
Volume
17
Issue
11
Publisher
Public Library of Science
Publisher Location
1160 BATTERY STREET, STE 100, SAN FRANCISCO, CA 94111 USA