Development of an effective clustering algorithm for older fallers

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...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:PloS one Ročník 17; číslo 11; s. e0277966
Hlavní autoři: Goh, Choon-Hian, Wong, Kam Kang, Tan, Maw Pin, Ng, Siew-Cheok, Chuah, Yea Dat, Kwan, Ban-Hoe
Médium: Journal Article
Jazyk:angličtina
Vydáno: United States Public Library of Science 28.11.2022
Public Library of Science (PLoS)
Témata:
Aged
Aged, 80 and over
Algorithms
Analysis
Balance
Biology and Life Sciences
Clinical decision making
Clinical medicine
Cluster Analysis
Clustering
Cognitive ability
Data collection
Datasets
Decision support systems
Embedding
Falls
Falls (Accidents)
Feature extraction
Feature selection
Gait
Health aspects
Health risks
Hospitals
Humans
Hypothesis testing
Medicine and Health Sciences
Missing data
Muscle Strength
Older people
Physical Sciences
Physiological aspects
Principal Component Analysis
Principal components analysis
Research and Analysis Methods
Risk analysis
Risk Factors
Risk groups
Statistical analysis
Stochasticity
Vector quantization
Malaysia
ISSN:1932-6203, 1932-6203
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí: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.
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
content type line 23
Competing Interests: The authors have declared that no competing interests exist.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0277966