An Exploration of Optimal Way to Integrate GAN with LSTM for Transmitting Healthcare Information

In recent years, transmitting health data from distant locations has posed significant challenges due to noise and inaccuracies in the data. Several advanced data processing algorithms have been developed to facilitate smoother transmission of health-related information by mitigating noise and addressing data integrity issues. This study compares the performance of various data processing algorithms for transferring health information, including linear correlation analysis (LCA), principal components analysis (PCA), wavelet transform, Fourier transform, Kalman filtering, and the integration of generative adversarial networks (GAN) with long short-term memory (LSTM). The results of this research indicate that the combination of GAN and LSTM is the most effective choice for the remote transmission of health data. The incorporation of the GAN network has significantly reduced redundant information in the dataset, resulting in improved accuracy of health data transmission. This research highlights the crucial role of advanced data processing techniques, particularly the integration of GANs and LSTMs, in ensuring the safe and accurate transmission of health data within remote healthcare environments. These findings are promising for enhancing health data transmission and contribute significantly to the improvement of remote healthcare services.