Development of an effective clustering algorithm for older fallers

Goh, Choon-Hian and Wong, Kam Kang and Tan, Maw Pin and Ng, Siew-Cheok and Chuah, Yea Dat and Kwan, Ban-Hoe (2022) Development of an effective clustering algorithm for older fallers. PLoS ONE, 17 (11). ISSN 1932-6203, DOI

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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.

Item Type: Article
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
Uncontrolled Keywords: Feature-Selection; Risk; Prevention; People; Gender
Subjects: H Social Sciences > HQ The family. Marriage. Woman
H Social Sciences > HV Social pathology. Social and public welfare
Divisions: Faculty of Engineering > Biomedical Engineering Department
Faculty of Medicine > Medicine Department
Depositing User: Ms. Juhaida Abd Rahim
Date Deposited: 17 Oct 2023 03:50
Last Modified: 24 Oct 2023 04:29

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