Objective To explore the risk factors for the occurrence of falls among elderly people in community, and to construct a Bayesian network prediction model of fall risk among elderly people in community. Methods A convenience sampling method was adopted to select 743 elderly people in community as the research subjects. Face⁃to⁃face questionnaire surveys were conducted to collect baseline data on falls. The research tools included as follows: Fried frailty phenotype, physical performance test, Pittsburgh sleep quality index, Short⁃Form Geriatric Depression Scale, Mini⁃Mental State Examination, and Nutritional Risk Screening 2002, etc. The fall outcomes were followed up one year later. The Netica 5.18 software was used to make the Bayesian network structure to construct the fall risk prediction model for the elderly, and Bayesian network inference was performed. The receiver operating characteristic curve was drawn to evaluate the prediction effect of the model. Results During the 1⁃year follow⁃up period, 81 elderly people fell, and the incidence rate of falls was 10.9 %. The Bayesian network prediction model of fall risk for the elderly in the community contained 14 nodes and 14 directed edges. Four key influencing factors were obtained by Bayesian network diagnostic inference, namely Instrumental Activity of Daily Living impairment (change rate 47.4%), time up and go test (change rate 39.2%), fall history (change rate 34.9%) and depression (change rate 31.0%). The Bayesian network model had a good fitting result (area under the curve =0.923), with a sensitivity of 0.864 and a specificity of 0.856. Conclusion There are many factors influencing the fall risk of the elderly in the community, among which Instrumental Activity of Daily Living impairment, timed up and go test, fall history and depression are the key influencing factors. The fall risk prediction model based on Bayesian network can intuitively describe the complex relationship between falls and influencing factors in the elderly, which has good predictive ability and can provide reference for the prevention of falls in the elderly in the community.

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