A Survey on Deep Learning Techniques for Predictive Analytics in Healthcare

Healthcare data is growing at more than 50% annually, making it one of the most rapidly expanding data in the digital world. Clinical problem-solving is a difficult skill that doctors must have in order to provide excellent care. This skill’s accuracy is critical to the patients’ lives and well-being. Using deep learning in the medical field may aid not only in enhancing classification accuracy but also in reducing diagnostic time and cost, as well as in disease prediction. Putting deep learning models into action requires utilizing a wide variety of programs and data sources. This study explores the existing research options and the challenges encountered in the field of deep learning in healthcare prediction. It presents a comparative and systemic study of deep learning and how it can be used for prediction in the healthcare domain. Furthermore, it provides a wide-ranging overview of the current deep learning methods used in healthcare prediction. As a result of this review, the total number of papers that were examined was 45 and covered the period from 2019 to 2023. This research compared the methodologies, strategies, datasets, and conclusions from the provided studies.