A Novel Hybrid LSTM-DNN Model for Ventilator Pressure Prediction: Comparative Analysis of Data Splitting Strategies

In recent years, deep learning has significantly transformed ventilator pressure forecasting, which is crucial for providing patients with safe, personalized respiratory therapy. This work introduces a novel hybrid model that combines Deep Neural Networks (DNN) with Long Short-Term Memory (LSTM) networks. To enhance forecast accuracy, we incorporated a special forget gate reset mechanism to this model. Our approach, which involved meticulous data collection and analysis, produced a robust model architecture that effectively captures the unique characteristics of each breath cycle. We examined various data-splitting techniques, particularly comparing Timeseries Split and K-fold cross-validation to determine the most effective one. According to our findings, Timeseries Split performs better at preserving the sequential order of the ventilator data. Notably, compared to traditional methods, our hybrid model achieved an astounding 84% reduction in Mean Absolute Error (MAE) and a 97 % reduction in Mean Squared Error (MSE). These results highlight the potential of our approach to greatly improve ventilator control by making precise, data-driven predictions.