Understanding of healthcare problems and solutions using deep learning

The nature of healthcare data is complex. It is not easy to interpret data by analyzing or manual process. Machine learning methodologies such as deep neural network models have grown more appealing in recent years in the healthcare sector. Machine learning (ML) algorithms are data analysis techniques that are efficient and effective at uncovering hidden patterns and other helpful information from large amounts of health data those conventional analytics cannot handle. On the other hand, deep learning (DL) approaches have been proved to be viable methodologies for pattern identification in healthcare systems. This chapter aims to consider DL methodologies for healthcare systems by reviewing recent trends, cutting-edge network topologies, applications, and industry developments. The initial goal is to provide a detailed understanding of how DL models are used in healthcare solutions to connect DL human healthcare interpretability and approaches. This chapter also focuses on the current and future open challenges and directions.