Cancer classifier models, machine learning systems and methods of use

A method, in a computer-implemented system comprising at least one processor and at least one memory, the at least one memory comprising instructions executed by the at least one processor to cause the at least one processor to implement one or more classifier models to predict an increased risk of having or developing cancer, for an asymptomatic patient, comprising:

• a) obtaining measured values of a panel of biomarkers in a sample from the patient, wherein a value of a biomarker corresponds to a level of the biomarker in the sample;
• b) obtaining clinical parameters corresponding to the patient including at least age and gender;
• c) classifying the patient into a risk category of having or developing cancer using a first classifier model, wherein the first classifier model is generated by a machine learning system using first training data that comprises values of a panel of at least two biomarkers, age, and a diagnostic indicator, for a population of patients;
• and,
• wherein the first classifier model classifies the patient in an increased risk category using input variables of age and the measured values of a panel of biomarkers from the patient when an output of the first classifier model is above a threshold;
• and,
• d) providing a notification to a user for diagnostic testing of the patient when the patient is classified in the increased risk category.