Weighted entropy deep features on hybrid RNN with LSTM for glucose level and diabetes prediction
Author:
Affiliation:
1. Assistant Professor Senior Grade, Information Technology, Bannari Amman Institute of Technology, Sathyamangalam, India
2. Professor, Information Technology, Bannari Amman Institute of Technology, Sathyamangalam, India
Publisher
Informa UK Limited
Subject
Computer Science Applications,Human-Computer Interaction,Biomedical Engineering,General Medicine,Bioengineering
Link
https://www.tandfonline.com/doi/pdf/10.1080/10255842.2022.2149263
Reference48 articles.
1. Deep convolutional neural network for diabetes mellitus prediction
2. Feature selection strategy based on hybrid crow search optimization algorithm integrated with chaos theory and fuzzy c-means algorithm for medical diagnosis problems
3. Testing of a Short-Term Blood Glucose Prediction Algorithm Using the DirecNet Database
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1. Hi-Le and HiTCLe: Ensemble Learning Approaches for Early Diabetes Detection Using Deep Learning and Explainable Artificial Intelligence;IEEE Access;2024
2. Clinical applications of artificial intelligence in diabetes management: A bibliometric analysis and comprehensive review;Informatics in Medicine Unlocked;2024
3. A Novel Hybrid Deep Learning Model for Blood Glucose Prediction with Extended Prediction Horizons in Type-1 Diabetic Patients;2023 International Conference on Emerging Research in Computational Science (ICERCS);2023-12-07
4. LSTMNCP: lie detection from EEG signals with novel hybrid deep learning method;Multimedia Tools and Applications;2023-09-18
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