Identification of Diabetic Patients through Clinical and Para-Clinical Features in Mexico: An Approach Using Deep Neural Networks

被引:9
作者
Alcala-Rmz, Vanessa [1 ]
Zanella-Calzada, Laura A. [1 ]
Galvan-Tejada, Carlos E. [1 ]
Garcia-Hernandez, Alejandra [1 ]
Cruz, Miguel [2 ]
Valladares-Salgado, Adan [2 ]
Galvan-Tejada, Jorge I. [1 ]
Gamboa-Rosales, Hamurabi [1 ]
机构
[1] Univ Autonoma Zacatecas, Unidad Acad Ingn Elect, Jardin Juarez 147, Zacatecas 98000, Zac, Mexico
[2] Hosp Especialidades Ctr Med La Raza, Unidad Invest Med Bioquim, Ctr Med Nacl Siglo XXI, Inst Mexicano Seguro Social, Ave Cuauhtemoc 330, Mexico City 06720, DF, Mexico
关键词
type; 2; diabetes; Artificial Neural Network; net reclassification improvement; computer-aided diagnosis; statistical analysis; DIAGNOSIS;
D O I
10.3390/ijerph16030381
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Diabetes is a chronic and noncommunicable but preventable disease that is affecting the Mexican population at worrying levels, being the first place in prevalence worldwide. Early diabetes detection has become important to prevent other health conditions that involve low organ yield until the patient death. Based on this problem, this work proposes the architecture of an Artificial Neural Network (ANN) for the automated classification of healthy patients from diabetics patients. The analysis was performed used a set of 19 para-clinical features to determine the health status of the patients. The developed model was evaluated through a statistical analysis based on the calculation of the loss function, accuracy, area under the curve (AUC) and receiving operating characteristics (ROC) curve. The results obtained present statistically significant values, with accuracy of 0.94 and AUC values of 0.98. Based on these results, it is possible to conclude that the ANN implemented in this work can classify patients with presence of diabetes from controls with significant accuracy, presenting preliminary results for the development of a diagnostic tool that can be supportive for health specialists.
引用
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页数:12
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