Optimizing Human Vitality - A Fuzzy Deep Learning Approach for Enhancing Organ Endurance in Healthcare

Abstract

The application of deep learning techniques in healthcare has shown promising results in improving patient outcomes. This study aims to optimize human vitality by enhancing organ endurance using a novel approach based on Fuzzy Variational Autoencoders (VAEs). Specifically, the focus is on diabetes and cardiac arrest datasets, two prevalent conditions that significantly impact organ function. The proposed framework leverages the power of deep learning and fuzzy logic to capture complex relationships and uncertainties inherent in healthcare data. By integrating fuzzy logic principles into the VAE architecture, the model can effectively handle imprecise and uncertain information associated with diabetes and cardiac arrest cases. The VAE framework is trained using a large dataset comprising medical records, clinical variables, and relevant biomarkers. Through an iterative training process, the Fuzzy VAE learns to encode the data of high-dimensional input into a latent space of lower-dimensional one while preserving the essential features and fuzzy relationships. Moreover, the enhanced organ endurance representations obtained from the Fuzzy VAE provide valuable insights into the underlying factors influencing the conditions, aiding in personalized treatment planning and decision-making. The results demonstrate that the Fuzzy VAE approach significantly improves the prediction accuracy and robustness compared to traditional deep learning models.