TRANSFER LEARNING BASED AUTO ENCODER FOR CLINICAL ORGAN VITALITY ENDURANCE PREDICTION

Abstract

Aim: This work implies that when deep learning methods are applied in the medical field, patients might get better outcomes.

Background: In this work, we enhance organ endurance and, hence, human vitality with a novel approach based on Transfer Learning Variational Autoencoders (VAEs).

Contribution: Diabetes and cardiac arrest are two frequent disorders that substantially impair organ function. This paper discusses the datasets of these diseases use deep transfer learning, the proposed method considers inherent complexity of medical data.

Methodology :Transfer Learning with VAE architecture, the model tends to manages erroneous and ambiguous data of both the cardiac arrest dataset and diabetes dataset. A large dataset with biomarkers, medical records and clinical factors, is considered essential for proper training of VAE. An iterative training is conducted for training the Transfer Learning and VAE to encode input data that are of high-dimensional into a lower-dimensional latent space. The components and the transfer learning connections improves the organ endurance that gets offered via Transfer Learning VAE and this helps to improve the treatment planning by offering the causes of the diseases.

Findings: The results shows that the Transfer Learning based on VAE significantly improves the resilience and accuracy than the traditional deep learning models.

Recommendation for Researchers:: This work can be recommended on developing a novel framework that uses deep learning algorithms to effectively optimize the provision of healthcare services and address these issues.

Future Research:This work can be enhanced using several deep learning algorithms for achieving better accuracy and performance.