Distal Symmetric Polyneuropathy Identification in Type 2 Diabetes Subjects: A Random Forest Approach

Healthcare

Irma Gonzalez-Curiel¹  Mónica Martínez-Acuña¹  Valeria Maeda-Gutiérrez²  CarlosE. Galván-Tejada²  Alejandra García-Hernández²  Hamurabi Gamboa-Rosales²  Huizilopoztli Luna-García²  JorgeI. Galván-Tejada²  Adan Valladares-Salgado³  Miguel Cruz³ 

[1] Unidad Académica de Ciencias Químicas, Universidad Autónoma de Zacatecas, Jardín Juarez 147, Centro, Zacatecas 98000, Mexico;Unidad Académica de Ingeniería Eléctrica, Universidad Autónoma de Zacatecas, Jardín Juarez 147, Centro, 98000 Zacatecas, Zac, Mexico;Unidad de Investigación Médica en Bioquímica, Hospital de Especialidades, Centro Médico Nacional Siglo XXI. Instituto Mexicano del Seguro Social. Mexico City, Av. Cuauhtémoc 330, Col. Doctores, CP 06720. Del. Cuauhtémoc, Ciudad de Mexico 06600, Mexico;

关键词: type 2 diabetes;    distal symmetric polyneuropathy;    feature selection;    boruta;    Random Forest;   

DOI  :  10.3390/healthcare9020138

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown