Disease Prediction Using Novel Deep Learning Mechanisms

Abstract

With the advent of cutting-edge computing tools, the health industry underwent a period of rapid change that resulted in an explosion of medical data and the emergence of new academic disciplines. There have been significant attempts made to both manage the influx of medical data and extract actionable insights from it. Digital records management, data integration, or computer-aided diagnosis and disease predictions are all areas where the healthcare business is struggling. Both goals, lowering healthcare costs and moving toward individualized care, require this. Scientists were thus motivated to employ cutting-edge tools such as big data analytics, data modeling, machine learning, and learning algorithms to mine data for actionable insights. To overcome these obstacles, deep learning provides numerous resources. Predictive analytics applied to health data is rapidly becoming a game-changing resource that makes it possible to provide patients with preventative care. This paper portrays the study of numerous deep learning methods and tools in practice, and it focuses on the deep learning in healthcare, with a particular emphasis on the framework for supervised neural data gathering to decision making.