This repository contains the code for our team's example Java implementation of a basic machine learning workflow. The purpose of the code in this example is to determine whether or not a patient has heart disease based on a number of common indicators (see here). The project leverages Weka to create and train the model, and attempts to follow the same software design patterns and best practices that might be found in a much larger Java ML project.
This project is built using Gradle. To run the project and show the accuracy results for the test data, simply import it into your favorite IDE as a Gradle project and execute the run task. This will build and train a decision tree model, then print cross validation results to the terminal. This should result in output similar to the following:
> Task :run
==== J48 Decision Tree Classifier ====
Total correct: 236/303
Percentage correct: 77.89%
Confusion matrix:
Predicted NO Predicted YES
Actual NO 107 31
Actual YES 36 129