Optimal Artificial Neural Network for the Diagnosis of Chagas Disease Using Approximate Entropy and Data Augmentation

Maria Fernanda Rodriguez, Diego Rodrigo Cornejo, Luz Alexandra Diaz, Antonio Ravelo-Garcia, Esteban Alvarez, Victor Cabrera-Caso, Dante Condori-Merma, Miguel Vizcardo Cornejo

Producción científica: Capítulo del libro/informe/acta de congresoContribución a la conferenciarevisión exhaustiva

Resumen

The use of machine learning for disease diagnosis is gaining popularity due to its ability to process data and provide accurate results, but optimazing it remains a challenge. Chagas disease is endemic in Latin America and has emerged as a health problem in more urban areas. Early and accurate diagnosis is essential to prevent cardiac complications, since an estimated 65 million people are at risk of contracting this disease. This study used a database of 292 subjects distributed into three groups: healthy volunteers (Control group), asymptomatic Chagasic patients (CHI group) and seropositive Chagasic patients with incipient heart disease (CH2 group). A densely connected neural network was used to classify them into their respective groups. The network received as input the Approximate Entropy values of each individual, which were calculated from the 24-hour circadian profiles every 5 minutes (288 RR subsegments). Time series data augmentation algorithms were applied during the training phase to improve the classification results. This approach allowed to achieve 100% accuracy and precision, validated by the ROC curve with AUC values of 1, proving to be a robust approach for early diagnosis and prevention of heart complications in Chagas disease.

Idioma originalInglés
Título de la publicación alojadaComputing in Cardiology, CinC 2023
EditorialIEEE Computer Society
ISBN (versión digital)9798350382525
DOI
EstadoPublicada - 2023
Evento50th Computing in Cardiology, CinC 2023 - Atlanta, Estados Unidos
Duración: 1 oct. 20234 oct. 2023

Serie de la publicación

NombreComputing in Cardiology
ISSN (versión impresa)2325-8861
ISSN (versión digital)2325-887X

Conferencia

Conferencia50th Computing in Cardiology, CinC 2023
País/TerritorioEstados Unidos
CiudadAtlanta
Período1/10/234/10/23

Nota bibliográfica

Publisher Copyright:
© 2023 CinC.

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