Live Demo: https://gauravEHRdemo.com
Project Video: https://www.youtube.com/watch?v=FDd4JziqupA
Developed a web application that provides intuitive predictions for Electronic Health Records using NodeJS, AngularJS, Big Data analytics to improve the insurance claim approval rate. It performs predictive analysis using a combination of K-Means Clustering, Naïve Baysian Classifier and Machine Learning. I was the Team Lead of this graduate project.
Technologies used- Big Data, NodeJS, Cassandra, Hadoop, HTML, CSS
Our project aims at reducing the insurance claim rejection rate by providing an intuitive prediction and suggestion system. Our project will analyze the EHR data and create a recommendation engine that will take into consideration the previous records and provide intuitive predictions and the suggestions while filling out forms based on the calculations done at runtime. The outcome will be an improved claim which is more likely to be approved and thus reduce the monetary losses.
Slideshow and Screenshots:
(Click on screenshot to view full page image)