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Multiple Disease Prediction System

This webapp was developed using Flask Web Framework and was deployed on Heroku server. The models used to predict the diseases were trained on large Datasets. The webapp can predict numerous diseases.

M

Multiple Disease Prediction System

This webapp was developed using Flask Web Framework and was deployed on Heroku server. The models used to predict the diseases were trained on large Datasets. The webapp can predict numerous diseases.

The problem Multiple Disease Prediction System solves

In the current time of pandemic people prefer to be home treated and to ensure they avoid contaminated places such as hospitals and clinics as much as possible. Our project aims to provides a user friendly platform to cross validate results at go and to spread general awareness and provide precautionary measures.
With the advancement in technologies and mobile phones being the most used user-friendly device, our team has come with an application that provides a prediction of the seven most caused lifestyle diseases like diabetes, cancer, and chronic diseases at your hand. Disease predictor allows you to make important predictions about an ongoing but unknown disease with a few pieces of information like symptoms and diagnostic reports. It also helps you to have an in-depth knowledge of the symptoms, causes, and other important factors for future reference.
With the pandemic time and regular busy schedule of the people, they find it difficult to make it to hospital or clinics to consults physicians for a regular checkup. Here is where we play an important role in helping people with home-based solutions. When collaborated with certified and qualified doctors the prediction and data produced as output become more accountable and flexible. With a vast amount of diverse data available over the internet, it's very obvious for people to confuse in choosing the correct piece of information. But with our app, this problem gets an easy solution.

Challenges we ran into

  • Processing various models was interesting but handling large datasets seemed to be a bit challenging.
  • Deciding on the UI was a bit complex within this short period of time hence we tried to keep it basic and simple.
  • An issue arose regarding image upload and model functionality but was handled effectively.

Discussion