Hybrid Optimization of CNN-LSTM Deep Learning Techniques for Effective Early Detection of Periodic Outbreaks from Health Data

The processing capabilities of CNNs are used in this paper to reach the goal of an early detection of periodic outbreaks using the health data through the proposed CNN-LSTM networks.The proposed CNN-LSTM networks involves a combination of CNNs with Long Short-Term Memory networks. CNN is the component that is run in the processing of the unprocessed data determining trends and regional dependencies. Patterns sequentially are then processed in the LSTM component and the dynamics of the temporal data is established because to an extent the CNN-LSTM model can establish long-term interdependency. The capability of the CNN-LSTM model was tested by the use of real-world health statistics on disease outbreaks. The information can be time-series data of the characteristics of the number of reported cases, location, and other essential variables. This is aimed at predicting and detecting the occurrence of recurring outbreaks with reasonable precision by training the CNN-LSTM model on this data and in this respect, the CNN-LSTM model is seen to be better than the rest of the deep learning models and traditional machine learning models. The cooperation between CNN and LSTM enables the model to be efficient in identifying recurrent outbreaks, and this is feasible since spatial and temporal patterns are present.