This is a final, diploma project which I have done in the framework of Technigo bootcamp for front-end development. The project aim was to create interactive application which was able to recognise symptoms of erly diabetes and estimate risk of disease development. Machine learning model of Random forest was build based on the clinical data published in UCI machine learning repository, Early stage diabetes risk prediction dataset. The more detailed information about dataset was published by Islam M.M.F., Ferdousi R., Rahman S., Bushra H.Y. (2020) Likelihood Prediction of Diabetes at Early Stage Using Data Mining Techniques. In: Gupta M., Konar D., Bhattacharyya S., Biswas S. (eds) Computer Vision and Machine Intelligence in Medical Image Analysis. Advances in Intelligent Systems and Computing, vol 992. Springer, Singapore. https://doi.org/10.1007/978-981-13-8798-2_12. Full stack application uses Python machine learning model and Flask REST API responsible for data processing. Front and backend parts (Express REST API) are build by Javascript.
The technology used: Python, Pandas, Scikit learn, Flask, Javascript, React, Redux, Node.js, Mongo DB, styling was done with CSS
Main problem was connection to between two programming languages.
The application is alive on Netlify server: https://nifty-johnson-c4308b.netlify.app