Attention-based BiLSTM-XGBoost model for reliability assessment and lifetime prediction of digital microfluidic systems.

ABSTRACT: Traditional methods for reliability and lifetime testing of digital microfluidic systems heavily rely on real-time monitoring data. This often leads to evaluation lag and limits their application, especially for complex droplets. To address these issues, this study proposes a novel prediction model for digital microfluidic (DMF) devices. The model combines an attention-based bidirectional long short-term memory (BiLSTM) with eXtreme Gradient Boosting (XGBoost) using a Stacking approach. This integrated model efficiently identifies the health state and predicts the failure time of digital microfluidic devices. This approach overcomes the limitations of traditional methods, such as over-reliance on sensor feedback and detection hysteresis. Experimental results demonstrate high prediction acc

SUBMITTER: He L 

PROVIDER: S-EPMC12453737 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature