The problem HealthFlow: Empowering Health Analytics with ML solves

In healthcare, analyzing extensive health reports is time-consuming and complex. HealthFlow simplifies this process using ML, enabling accurate predictions. However, challenges like data complexity and interpretability persist.

HealthFlow pre-processes health data, utilizes ML algorithms, ensures interpretability, and validates predictions. This empowers healthcare with time and cost savings, improved decision-making, personalized medicine, research advancements, and patient empowerment.

HealthFlow's predictive analytics model for health management identifies at-risk individuals early, tailors interventions, optimizes resources and enables long-term monitoring.

HealthFlow exemplifies ML's potential in revolutionizing healthcare analytics, addressing challenges, and enhancing patient outcomes.

Challenges we ran into

At first, we tried to virtualize eye testing through a website, using AR, but due to some issues with adjusting the focal length of the lens of the eye at a distance, and at the same time, taking the user input, we couldn't come up with a simple and elegant solution for this problem.
Then we came up with the idea of automating the prediction of certain diseases by taking some parameters, using some kind of model designed on the already available dataset, which will be done through the help of a website
Then we ran into a problem where the result from our ML model couldn't be displayed on the website due to the presence of cors error and because of some issues in the code which occurred while we were using Flask to display the result from Python to the Web.
After help from the mentors from HackNITR, we were able to solve the cors error, but still, the result couldn't be displayed due to a server error which we were not able to fix in the final submission, but we will definitely work on it in the future to improve the website and also removing the bugs which were present