Adaptive Buffering Strategies for Incremental Learning Under Concept Drift in Lifestyle Disease Modeling

Lifestyle diseases such as diabetes manifest through subtle and non-stationary clinical patterns, posing significant challenges for real-time prediction and monitoring. Conventional machine learning models often struggle to maintain performance under evolving data distributions due to concept drift....

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Auteurs principaux:	B. S. Prashanth, M. V. Manoj Kumar, B. H. Puneetha, Nasser Abdo Saif Almuraqab, Ariful Hoque, Immanuel Azaad Moonesar, Ananth Rao
Format:	Artigo
Langue:	Inglês
Publié:	IEEE 2025-01-01
Collection:	IEEE Access
Sujets:	Adaptive buffer Bayesian neural network (BNN) bi-directional LSTM (BiLSTM) centers for disease control and prevention (CDC) concept drift detection and adaptation first in first out (FIFO)
Accès en ligne:	https://ieeexplore.ieee.org/document/11187339/
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