Remote patient monitoring has become pivotal in managing chronic diseases like diabetes. This study proposes a novel approach for the classification of diabetes subtypes utilizing a deep-learning reconstruction algorithm. The system leverages continuous patient data obtained through remote monitoring devices, enabling real-time analysis for timely intervention. The deep-learning reconstruction algorithm, based on a convolutional neural network architecture, demonstrated exceptional accuracy in distinguishing between diabetes subtypes. The model achieved an overall classification accuracy of 92%, outperforming traditional methods. It exhibited high sensitivity and specificity, with values exceeding 90% for each subtype. The results showcase the system’s effectiveness in classifying diabetes subtypes: Type 1 diabetes (Sensitivity: 94%, Specificity: 92%), Type 2 diabetes (Sensitivity: 91%, Specificity: 94%), and Gestational diabetes (Sensitivity: 93%, Specificity: 91%). The system’s ability to accurately identify these subtypes ensures personalized and targeted care for patients.
Callins Christiyana Chelladurai1, Punitha Murugesan2, Sivajothi Esakkimani3, Selvi Shanmuga Pandian4
SRM Madurai College for Engineering and Technology, India1, Sethu Institute of Technology, India2,4, Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology, India3
Deep-Learning Reconstruction Algorithm, Diabetes Subtypes, Remote Patient Monitoring, Convolutional Neural Network, Healthcare Classification
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| Published By :
ICTACT
Published In :
ICTACT Journal on Soft Computing
( Volume: 14 , Issue: 3 , Pages: 3249 - 3254 )
Date of Publication :
January 2024
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