Inspiration 💡

We know due to covid, our daily lives have changed drastically. SO is our health. Nowadays, everything is online and in virtual mode. Which reminds me of how we used to go to the doctors to check what diseases we have, which was very time-consuming. So, we build this application to help people check what diesels they have virtually.

What it does ❓

To reduce the burden on offline health centers. The majority of them were terrified to leave their homes. Even for a basic checkup, people were hesitant to go. The goal of this project is to come up with a viable solution to this problem. The patient must first submit their symptoms, after which our Machine-Learning-based Web Application will inform them of the ailment they are experiencing and recommend some home cures and medications. We will also send an SMS and E-MAIL to the patient informing them of the ailment, as well as its treatments and medications. We've also included a feature that allows the patient to see the nearest hospital to their current location. The major goal of this initiative is to educate patients about the ailment they are suffering from and to provide appropriate treatment options, as well as to reduce the burden on offline health centers, clinics, and hospitals.

How we built it 🔧🔨

For the front end part, we have used basic HTML, CSS. To predict the diseases we have build our own machine learning models using Scikit-learn and w have obtained the required data for the model form kaggle. For the backend part, we have used python flask framework. And we have deployed our web-application on Heroku.

Challenges we ran into 🏃‍♂️

There are various challenges which we face during the execution of this project.

  1. Building our own machine learning model with the huge dataset from kaggle from scratch was really tough. Plus, we never have used Scikit-learn before.

  2. Sending SMS and mail with Twilio and SendGrid was a real problem, we face many problems in this particular task.

3.how to integrate the machine learning model with flask backend and deploying the web-app on internet were also challenges.

Accomplishments that we're proud of 🏆

We both are proud of ourselves that we are able to complete this project. This project is one of our milestones which we have achieved. We have never used flask and made our own machine learning model before, and to be able to achieve this result is such a great achievement for us.

What we learned 🧠

  1. We learned how to use flask at backend to make web applications.

  2. We learned how to make our own machine learning model with Scikit-learn and dataset which we have taken from Kaggle.

  3. Furthermore, we leaned how to integrate a machine learning model into a web application.

  4. We learn how to use Twilio API to send SMS, messages and email (using SendGrid) to others.

  5. We learn how to deploy our web application on internet (Which we did with Heroku) and how to use DNS.

What's next for MedLyfe ⏭

In the future I want to add features like online counselling where the doctor and patient can interact with each other, I would also like to add online memories reminders etc. I will make it open source to gain feedback from developers and improve my website.

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