用机器学习预测50岁及以上巴西人的全因死亡率:巴西老龄化纵向研究(ELSI-Brazil)的结果。

IF 4.1 Q2 GERIATRICS & GERONTOLOGY
Felipe Mendes Delpino, Alexandre Dias Porto Chiavegatto Filho, Juliana Lustosa Torres, Fabíola Bof de Andrade, Maria Fernanda Lima-Costa, Bruno Pereira Nunes
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引用次数: 0

摘要

我们的目标是开发一个机器学习模型来预测50岁及以上巴西人的全因死亡率,并将人口统计学、健康和生活方式特征作为预测因素。我们分析了巴西老龄化纵向研究(ELSI-Brazil)第一和第二波(2015-2021)的数据,这是一个来自巴西五个地区70个城市的全国代表性样本。9种算法,包括随机森林、梯度增强、XGBOOST和逻辑回归,在9412名参与者(54.6%的女性)中进行了测试,在大约5年的时间里记录了970例死亡。使用59个预测变量,我们用AUC、准确度、精度和F1-Score等指标评估性能。随机森林的AUC为0.92 (95% CI: 0.90-0.94)。SHAP分析强调,年龄、性别、身体质量指数、药物使用和身体活动是最重要的预测因素。将这些模式纳入卫生保健系统可以改进政策规划并实现有针对性的干预,最终为老龄人口带来更好的健康结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Predicting all-cause mortality with machine learning among Brazilians aged 50 and over: results from The Brazilian Longitudinal Study of Ageing (ELSI-Brazil).

We aimed to develop a machine-learning model to predict all-cause mortality among Brazilians aged 50 and over, incorporating demographic, health, and lifestyle characteristics as predictors. We analyzed data from the Brazilian Longitudinal Study of Aging (ELSI-Brazil), waves 1 and 2 (2015-2021), a nationally representative sample from 70 municipalities across Brazil's five regions. Nine algorithms, including Random Forest, Gradient Boosting, XGBOOST, and Logistic Regression, were tested on 9412 participants (54.6% female), with 970 deaths recorded over approximately five years. Using 59 predictor variables, we assessed performance with metrics like AUC, accuracy, precision, and F1-Score. Random Forest excelled with an AUC of 0.92 (95% CI: 0.90-0.94). SHAP analysis highlighted age, sex, BMI, medication use, and physical activity as top predictors. Integrating these models into healthcare systems can improve policy planning and enable targeted interventions, ultimately fostering better health outcomes for aging populations.

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