A Novel Predictive Analysis to Identify the Weather Impacts for Congenital Heart Disease Using Reinforcement Learning

Reinforcement learning is a powerful approach for predictive analysis to identify the weather impacts for congenital heart disease. The key advantages of this method include the utilization of sensor data to predict the weather conditions on a daily basis and the ability to learn from feedback and adapt the predictions over time. In this paper, an innovation model has proposed by using reinforcement learning algorithm. It can gain important insights regarding the impact of weather on congenital heart disease. The predictive models developed using this approach have the potential to help medical professionals as well as the general public in better predicting and managing congenital heart disease. This could help reduce costs associated with care and ultimately improve health outcomes.