Fong Kenneth N K, Chung Raymond C K, Sze Patrick P C, Ng Carmen K M
Department of Rehabilitation Sciences, The Hong Kong Polytechnic University, Hong Kong SAR.
Elderly Resources Centre, Hong Kong Housing Society, Hong Kong SAR.
Digit Health. 2023 Jun 7;9:20552076231181202. doi: 10.1177/20552076231181202. eCollection 2023 Jan-Dec.
To examine the predictive attributes for accidental falls in community-dwelling older people in Hong Kong using decision tree analysis.
We recruited 1151 participants with an average age of 74.8 years by convenience sampling from a primary healthcare setting to carry out the cross-sectional study over 6 months. The whole dataset was divided into two sets, namely training set and test set, which respectively occupied 70% and 30% of the whole dataset. The training dataset was used first; decision tree analysis was used to identify possible stratifying variables that could help to generate separate decision models.
The number of fallers was 230 with 20% 1-year prevalence. There were significant differences in gender, use of walking aids, presence of chronic diseases, and co-morbidities including osteoporosis, depression, and previous upper limb fractures, and performance in the Timed Up and Go test and the Functional Reach test among the baselines between the faller and non-faller groups. Three decision tree models for the dependent dichotomous variables (fallers, indoor fallers, and outdoor fallers) were generated, with overall accuracy rates of the models of 77.40%, 89.44% and 85.76%, respectively. Timed Up and Go, Functional Reach, body mass index, high blood pressure, osteoporosis, and number of drugs taken were identified as stratifying variables in the decision tree models for fall screening.
The use of decision tree analysis for clinical algorithms for accidental falls in community-dwelling older people creates patterns for decision-making in fall screening, which also paves the way for utility-based decision-making using supervised machine learning in fall risk detection.
采用决策树分析方法，研究香港社区老年人意外跌倒的预测因素。
通过便利抽样从基层医疗保健机构招募了1151名平均年龄为74.8岁的参与者，进行为期6个月的横断面研究。将整个数据集分为训练集和测试集，分别占整个数据集的70%和30%。首先使用训练数据集；采用决策树分析来识别可能有助于生成单独决策模型的分层变量。
跌倒者人数为230人，1年患病率为20%。跌倒者组和非跌倒者组在基线时的性别、助行器使用情况、慢性病的存在情况、合并症（包括骨质疏松症、抑郁症和既往上肢骨折）以及计时起立行走测试和功能性前伸测试的表现存在显著差异。生成了三个用于二分依赖变量（跌倒者、室内跌倒者和室外跌倒者）的决策树模型，模型的总体准确率分别为77.40%、89.44%和85.76%。计时起立行走测试、功能性前伸测试、体重指数、高血压、骨质疏松症和服药数量被确定为跌倒筛查决策树模型中的分层变量。
将决策树分析用于社区老年人意外跌倒的临床算法，可为跌倒筛查创建决策模式，这也为在跌倒风险检测中使用监督式机器学习进行基于效用的决策铺平了道路。
