PariCov

Inspiration

We were invigorated to create a website that could provide people with the required testing techniques with just a few clicks. We aim to assist people detect Parkinson's disease at early stage to increase chances to control symptoms.

What it does

Our website PariCov is a platform that works towards helping people detect Parkinson’s disease at early stage by providing them with three handwriting recognition detectors. The test includes Circle test, “ellel” test and "Spiral " test. In Circle Test we will be instructed to retrace the path drawn there, after done retracing click on next button. Now we will be redirected to the “ellel” test where we have to retrace the spiral path and click on the next button. The last test Lemme test will instruct you to draw a straight line and click on submit button. Now we will be redirected to the “ellel” test where we have to retrace the spiral path and click on the next button.On clicking submit option you will be provided with your reports. This section will show you the categories, for example if it shows more than 80% ,then you are likely to suffer from Parkinson’s disease and if it’s below that then you are out of risk.

How we built it

While designing the project, we thought about which Ml model we could use. By following the, research papers published on Volume 19 Supplement 9s and Proceedings of the 2018 International Conference on Intelligent Computing (ICIC 2018) and Intelligent Computing and Biomedical Informatics (ICBI) 2018 conference: medical informatics and decision making we get deep knowledge of test techniques which can be used in detecting Parkinson’s disease at early stage. Using ReactJS, Javascript, HTML, Google forms, CSS, Ml algorithm we were able to design this project and make it as user-friendly as possible. The experience of building this solution was indeed a result of the team effort, daily stand-ups, planning sessions, refinements, and reviews at the end of the day.

Challenges we ran into

The main challenge was to collect accurate data and statistics as cases can easily be reported in a developed countries in comparison to a developing countries. Once we had done with data collection, finding the accurate Ml model was the next challenging part which we came across. We had to go through and understand alots of Ml models to find the most accurate one.

Accomplishments that we're proud of

We are proud of the solution we built in how we hope it can contribute to society. We tried to construct our solution as feasible , scalable and accurate as possible. We are proud that we could use our experience to build a solution that could benefit humanity and contribute to a better tomorrow.

What we learned

We learned how to implement a machine learning model and making it accessible to the public for use , how to solve a real-life problem in the present situation, and the complexity behind planning an appropriate website to meet people's needs. Both of us worked together on designing our Website on Figma, helped correct the flaws in each other's work. We had meets lined up to discuss the project which enhanced our communication skills. We found the need to support our community and learned how to rift the bridge between technology and humanity.

What's next for PariCov

We also have future plan ready to work on, the search for the availability of more test techniques where people could visit this site and get themselves self assess. We are also working on report formats to make understand and analyze it. We want to make GUI of this application much better and also we will be adding more data so that we could have more tests and would we able to give much accurate results so that person suffering gets to know at what extent this disease has affected them.

Built With

• css
• google-docs
• html
• javascript
• ml
• react-js