In Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology
BACKGROUND : Limited studies have focused on the risk assessment of stroke in rural regions. Moreover, the application of artificial intelligence in stroke risk scoring system is still insufficient. This study aims to develop a simplified and visualized risk score with good performance and convenience for rural stroke risk assessment, which is combined with a machine learning (ML) algorithm.
METHODS : Participants of the Henan Rural Cohort were enrolled in this study. The total participants (n = 38,322) were randomly split into a train set and a test set in the ratio of 7:3. An ML algorithm was used to select variables and the logistic regression was then applied to construct the scoring system. The C-statistic and the Brier score (BS) were used to evaluate the discrimination and calibration. The Framingham stroke risk profile (FSRP) and the self-reported stroke risk function (SRSRF) were chosen to be compared.
RESULTS : The Rural Stroke Risk Score (RSRS) was produced in this study, including age, drinking status, triglyceride, type 2 diabetes mellitus, hypertension, waist circumference, and family history of stroke. On validation, the C-statistic was 0.757 (95% CI 0.749-0.765) and the BS was 0.058 in the test set. In addition, the discrimination of RSRS was 6.02% and 7.34% higher than that of the FSRP and SRSRF, respectively.
CONCLUSIONS : A well-performed scoring system for assessing stroke risk in rural residents was developed in this study. This risk score would facilitate stroke screening and the prevention of cardiovascular disease in economically underdeveloped areas.
Ding Zhongao, Zhang Liying, Niu Miaomiao, Zhao Bo, Liu Xiaotian, Huo Wenqian, Hou Jian, Mao Zhenxing, Wang Zhenfei, Wang Chongjian
2023-Jan-19
Machine learning, Risk score, Rural regions, Screening, Stroke
