Given the critical importance of decision-making speed in healthcare, using the superiority of deep learning techniques to predict disease as quickly as possible is an efficient way to prevent or reduce mortality and physical disabilities. In this study, we try to understand the application of some supervised deep learning based architectures in disease prediction using medical data. Main networks that will be studied are based on long-short term memory (LSTM) neural networks that, in addition to single data, can accept sequence data as input. The main goal of this study is to understand how LSTM mechanisms can be used to solve the problem of disease prediction using healthcare data.
