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Updated: Jan 15, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
Construction of decision tree prediction model and group intervention for the mindfulness level of community
Hao Zheng1, Junrong Xu2, Fei Li3
1Faculty of Nursing, Guangxi University of Chinese Medicine, Nanning, Guangxi, China.
Abstract:
Pre-frailty, a reversible state before frailty, is key for intervention. Mindfulness, a protective factor against frailty, is underexplored in community-dwelling pre-frail older adults. This study identified its influences, built predictive models, and proposed stratified interventions. 156 pre-frail older adults meeting inclusion and exclusion criteria were recruited from 5 communities in Nanning City, Guigang City, and Yulin City between August 2024 and February 2025 via convenience sampling. Data were collected using a General Demographic Questionnaire, the Chinese Frailty Scale, and the Mindful Attention Awareness Scale (Chinese version). Univariate analysis and multiple linear stepwise regression were employed to identify influencing factors, and a decision tree algorithm was applied to construct the prediction model. The mean mindfulness score among community-dwelling pre-frail older adults was 70.46 ± 9.80 (95% CI: 68.91-72.01), indicating a moderate level. Multiple linear stepwise regression identified monthly income as the primary influencing factor (β = 0.208, P = .011). Secondary factors significantly associated with mindfulness levels were medical burden, place of residence, and insomnia, with no evidence of multicollinearity (P < .05, VIF < 5). The decision tree model comprised 4 layers, 11 nodes, and 6 terminal nodes. Monthly income emerged as the most significant predictor (root node F = 18.645, P < .001), using healthcare burden, insomnia status, number of children, and educational attainment as additional classification nodes. Both methods consistently verified monthly income as the primary predictor. Establishing a targeted intervention framework integrating "Economic Support-Sleep Management-Intergenerational Support" was recommended. prioritizing support for the low-income group with medical burdens through integrated measures including economic assistance, pain management, bundled medical-psychological services, and community care centers; promoting "sleep-mindfulness" combined interventions and adapting urban community support models to rural settings to bridge urban-rural gaps; These findings provide evidence-based guidance for enhancing mental health and delaying frailty progression in this population.
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