Revolutionizing predictive analytics: A hybrid CNN‐LSTM approach to predicting cognitive status using driving behavior analysis

This study underscores the efficacy of hybrid CNN-LSTM models for capturing nonlinear and temporal dependencies in cognitive functioning and driving behavior data. Feature engineering, combined with advanced modeling techniques, enhances predictive capabilities and reveals nuanced relationships in the data. Future research could explore alternative architectures, such as attention mechanisms, and integrate external factors like weather or traffic conditions to improve predictive accuracy further. This innovative approach demonstrates significant potential for broader applications in predictive analytics involving sequential data.