Abstract: In this invention, prediction on the predisposition of a human test subject to cancer is made based on machine learning-assisted comparison of the copy number variations (“CNV”) found in the constitutional DNA of the test subject with a set of diagnostic recurrent CNV features (viz. markers) selected from a collection of constitutional DNA samples from noncancer subjects (designated as “Noncancer DNA” samples) plus constitutional DNA samples from cancer patients (designated as “Cancer DNA” samples), all from the same ethnic group as the test subject. Selection and testing of the set of diagnostic recurrent CNV features is performed using a machine learning procedure, exemplified by the CFS-based method, the Frequency-based method and the Classifier-based method, together with the Naïve Bayes classification method. Prediction of the test subject's predisposition to cancer is also performed with the Naïve Bayes classification method.