Overview / Usage

A supervised machine learning classifier based on Voting Feature Intervals (VFI) algorithm
is developed for the detection of the type of cardiac arrhythmia on a standard UCI
dataset. Five different VFI techniques were implemented on the ECG data obtained from
the UCI repository. A few popular algorithms such as Naïve Bayes, Decision Tree, Support
Vector Machines (SVM) and k­Nearest Neighbour (kNN) were also implemented. The
accuracy of each algorithm was validated using k­fold cross validation and plotted using
MATLAB. The accuracies were compared to choose the best algorithm for this particular
problem.