Due to the massive increase in medical documents every day (including books, journals, blogs, articles, doctors' instructions and prescriptions, emails from patients, etc.), it is becoming very challenging to handle and to categorize them manually. One of the most challenging projects in information systems is extracting information from unstructured texts, including medical document classification. The discovery of knowledge from medical datasets is important in order to make effective medical diagnosis. Developing a classification algorithm that classifies a medical document by analyzing its content and categorizing it under predefined topics is the primary aim of this research. In this project work we were able to succeed in applying Natural Language Processing which is a branch of Machine Learning to Classifying Health related documents. We made use of the OpenNLP Application Programming Interface which is a Java API for training a model and classifying the documents. We make use of Materialize which is a HTML5, CSS and JavaScript framework for building the user interface. The software is also built using the Model-View-Controller (MVC) architecture. The algorithm classified the articles correctly under the actual subject headings and got the total subject headings correct. This holds promising solutions for the global health arena to index and classify medical documents expeditiously.