Leveraging Deep Learning Models for Early Detection of Chronic Diseases in Healthcare

Abstract

The early detection of chronic diseases plays a crucial role in improving patient outcomes and reducing healthcare costs. Deep learning, a subset of artificial intelligence, has shown promise in analyzing complex healthcare data for early disease detection. This paper explores the use of deep learning algorithms, such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs), to identify early signs of chronic diseases in healthcare. We discuss the challenges and opportunities associated with leveraging deep learning models for early disease detection, including data privacy concerns, model interpretability, and scalability. Additionally, we highlight key studies and advancements in the field and provide recommendations for future research directions.