The set of attributes we created was used as input to train our model. We tested different classification algorithms of WEKA to see which produced the highest classification accuracy. Our goal was also to optimize the area under the receiver-operating characteristic (ROC) curve, accuracy, precision, true-positive rate, false-positive rate, and area under the precision-recall (AUC-PR) curve.