Pretrained Model: A Transfer Learning Approach for Early Prediction of Chronic Diseases

Abstract

Transfer learning, mainly through pre-trained models, has gained traction as a promising approach for the early prediction of chronic diseases using medical data. In this study, we investigate the challenges and complexities associated with applying pretrained models in the healthcare domain. Leveraging a pre-trained model trained on diverse datasets, we explore the transferability of knowledge to the field of chronic disease prediction. This study emphasizes the role of the pre-trained model and transfers Learning their advantages and disadvantages as well as reveals several critical challenges in the healthcare sector.