Heart disease is the leading cause of death in the United States. Every year, one out of every four deaths is due to heart disease. Moreover, the medical costs for treating heart disease is notoriously high, each year costing more than $363 billion.  The rate of recovery is so much faster and the medical procedures to cure heart disease are so much less if doctors could detect heart disease at an early stage.
Therefore, we created a scalable application that can asses the likelihood of multiple different diseases, like heart disease or breast cancer by implementing different machine learning models. We developed a welcoming UI for doctors to not only monitor their patients' risks, but also allows them to schedule, message, and experiment with parameters associated with high disease risk.
Throughout this project, we learned a lot about the convergence of different models, analyzing with different models, the application of Google cloud, and even how to create a friendly user interface.