Healthify - Web Application for Disease Prediction with Machine Learning

This web application was developed as a project for the Big Data Analytics course in the Information and Communication Technologies Engineering degree. The focus of the application is to use a set of specialized technologies for data analysis and the creation of Machine Learning models. Among the tools used are the Python programming language and some essential libraries such as Scikit-learn, Pandas, NumPy, and Matplotlib.

Once the Machine Learning models were built, they were trained using datasets stored in a non-relational MongoDB database. In addition, I created a RESTful API using Flask to connect the Front-End application with the Machine Learning models, and later Dockerized the application to deploy the server on an AWS EC2 instance.

With the server ready to listen to HTTP requests in production and perform predictions, the next step was to develop the Front-End of the website, for which I used the React Next.js framework, which allowed me to quickly create a scalable and production-ready web application. This entire set of tools and technologies enabled me to build a web application capable of predicting diseases such as diabetes, hypertension, and cardiovascular conditions, using Machine Learning models trained with real data.

This project allowed me to learn a lot about the use of Machine Learning technologies and their application in real life, from techniques for data collection and selection to the creation of Machine Learning models and their implementation in a web application.

But I don’t just take with me everything I learned from developing this project, but also the process that allowed me to reach the final result, since this project is the outcome of extensive research, planning, and teamwork. Moreover, it was a project we presented in front of a jury of expert professors in the field, which greatly helped me improve my communication and project presentation skills.