1. A method for predicting information relating to a coronary lesion, the method comprising: acquiring, for each of a plurality of individuals, a geometric model, blood flow characteristics, and plaque information for at least part of each individual's vascular system; identifying, for each of a plurality of locations in the geometric models of the plurality of individuals, features associated with a probability of a presence of plaque within the geometric models, the probability of the presence of plaque being determined by an analysis of the intensity distribution of individual-specific image data, by an image processor; forming feature vectors comprising blood flow characteristics at each of the plurality of locations and information related to each of the plurality of locations in the geometric models, for each of the plurality of individuals; associating the feature vectors with the identified features associated with the probability of the presence of plaque within the geometric models, for each of the plurality of individuals; training a machine learning algorithm to predict the probability of the presence of plaque within a geometric model, from feature vectors comprising blood flow characteristics at a plurality of locations on a geometric model and information related to each of the plurality of locations, using the associated feature vectors; acquiring, for a patient, non-invasively obtained patient-specific image data of structures of the patient's anatomy and blood flow characteristics for each of a plurality of locations for at least part of the patient's vascular system; segmenting the received patient-specific image based on regions with known blood flow characteristics; generating a geometric model of the patient for at least part of the patient's vascular system using the segmented patient-specific image data, the geometric model including the plurality of locations and blood flow characteristics at each of the plurality of points; forming feature vectors comprising blood flow characteristics at each of the plurality of locations and information related to each of the plurality of locations in the geometric model of the patient; executing the trained machine learning algorithm on the patient's geometric model and blood flow characteristics to predict the probability of the presence of plaque within the geometric model of the patient, using the formed feature vectors; and determining, non-invasively, a patient-specific prediction or a patient-specific probability of a development of artery disease for at least one location in the patient's geometric model using the predicted probability of the presence of plaque within the geometric model of the patient.