This work investigates coronary artery disease (CAD) prediction from electrocardiogram (ECG) data taking into account different windows with respect to the time of diagnosis. We report that ECG waveform measurements automatically collected during ECG recordings contain sufficient features for good classification of CAD using machine learning models up to five years before diagnosis. On the other hand, convolutional neural networks trained on the ECG signals themselves appear to best extract CAD related features when processing data collected one year after a diagnosis is made. Through this work we demonstrate that the type of ECG data and the time window with respect to diagnosis should guide model selection.