Methods and machine learning systems for predicting the likelihood or risk of having cancer

Abstract

Embodiments of the present invention relate generally to non-invasive methods and tests that measure biomarkers (e.g., tumor antigens) and collect clinical parameters from patients, and computer-implemented machine learning methods, apparatuses, systems, and computer-readable media for assessing a likelihood that a patient has a disease, relative to a patient population or a cohort population. In one embodiment, a classifier is generated using a machine learning system based on training data from retrospective data and subset of inputs (e.g. at least two biomarkers and at least one clinical parameter), wherein each input has an associated weight and the classifier meets a predetermined Receiver Operator Characteristic (ROC) statistic, specifying a sensitivity and a specificity, for correct classification of patients. The classifier may then be used to assesses the likelihood that a patient has cancer relative to a population by classify the patient into a category indicative of a likelihood of having cancer or into another category indicative of a likelihood of not having cancer.