Maeda-Gutiérrez Valeria, Galván-Tejada Carlos E, Cruz Miguel, Valladares-Salgado Adan, Galván-Tejada Jorge I, Gamboa-Rosales Hamurabi, García-Hernández Alejandra, Luna-García Huizilopoztli, Gonzalez-Curiel Irma, Martínez-Acuña Mónica
Unidad Académica de Ingeniería Eléctrica, Universidad Autónoma de Zacatecas, Jardín Juarez 147, Centro, Zacatecas 98000, Mexico.
Unidad de Investigación Médica en Bioquímica, Hospital de Especialidades, Centro Médico Nacional Siglo XXI. Instituto Mexicano del Seguro Social, Av. Cuauhtémoc 330, Col. Doctores, Del. Cuauhtémoc, Mexico City 06720, Mexico.
Healthcare (Basel). 2021 Feb 1;9(2):138. doi: 10.3390/healthcare9020138.
The prevalence of diabetes mellitus is increasing worldwide, causing health and economic implications. One of the principal microvascular complications of type 2 diabetes is Distal Symmetric Polyneuropathy (DSPN), affecting 42.6% of the population in Mexico. Therefore, the purpose of this study was to find out the predictors of this complication. The dataset contained a total number of 140 subjects, including clinical and paraclinical features. A multivariate analysis was constructed using Boruta as a feature selection method and Random Forest as a classification algorithm applying the strategy of K-Folds Cross Validation and Leave One Out Cross Validation. Then, the models were evaluated through a statistical analysis based on sensitivity, specificity, area under the curve (AUC) and receiving operating characteristic (ROC) curve. The results present significant values obtained by the model with this approach, presenting 67% of AUC with only three features as predictors. It is possible to conclude that this proposed methodology can classify patients with DSPN, obtaining a preliminary computer-aided diagnosis tool for the clinical area in helping to identify the diagnosis of DSPN.
全球糖尿病患病率正在上升，对健康和经济造成影响。2型糖尿病的主要微血管并发症之一是远端对称性多发性神经病变（DSPN），在墨西哥影响了42.6%的人口。因此，本研究的目的是找出这种并发症的预测因素。数据集包含总共140名受试者的临床和辅助临床特征。使用Boruta作为特征选择方法，随机森林作为分类算法，采用K折交叉验证和留一法交叉验证策略构建多变量分析。然后，通过基于敏感性、特异性、曲线下面积（AUC）和受试者工作特征（ROC）曲线的统计分析对模型进行评估。结果显示，采用这种方法的模型获得了显著值，仅用三个特征作为预测因素时AUC为67%。可以得出结论，这种提议的方法能够对DSPN患者进行分类，为临床领域获得一种初步的计算机辅助诊断工具，以帮助识别DSPN的诊断。
