Hernandez-Laredo Enrique, Estévez-Pedraza Ángel Gabriel, Santiago-Fuentes Laura Mercedes, Parra-Rodríguez Lorena
Tianguistenco Professional Academic Unit, Autonomous University of the State of Mexico, Tianguistenco 52640, Mexico.
Health Science Department, Metropolitan Autonomous University, Mexico City 09310, Mexico.
Bioengineering (Basel). 2024 Sep 11;11(9):908. doi: 10.3390/bioengineering11090908.
The aim of this study was to improve the diagnostic ability of fall risk classifiers using a Bayesian approach and the Simulated Annealing (SA) algorithm. A total of 47 features from 181 records (40 Center of Pressure (CoP) indices and 7 patient descriptive variables) were analyzed. The wrapper method of feature selection using the SA algorithm was applied to optimize the cost function based on the difference of the mean minus the standard deviation of the Area Under the Curve (AUC) of the fall risk classifiers across multiple dimensions. A stratified 60-20-20% hold-out method was used for train, test, and validation sets, respectively. The results showed that although the highest performance was observed with 31 features (0.815 ± 0.110), lower variability and higher explainability were achieved with only 15 features (0.780 ± 0.055). These findings suggest that the SA algorithm is a valuable tool for feature selection for acceptable fall risk diagnosis. This method offers an alternative or complementary resource in situations where clinical tools are difficult to apply.
本研究的目的是使用贝叶斯方法和模拟退火（SA）算法提高跌倒风险分类器的诊断能力。对来自181条记录的47个特征（40个压力中心（CoP）指标和7个患者描述变量）进行了分析。应用基于SA算法的特征选择包装方法，基于跌倒风险分类器在多个维度上的曲线下面积（AUC）的均值减去标准差的差异来优化成本函数。分别使用分层的60-20-20%留出法用于训练集、测试集和验证集。结果表明，虽然使用31个特征时观察到最高性能（0.815±0.110），但仅使用15个特征时实现了更低的变异性和更高的可解释性（0.780±0.055）。这些发现表明，SA算法是用于可接受的跌倒风险诊断的特征选择的有价值工具。在临床工具难以应用的情况下，该方法提供了一种替代或补充资源。
