This method uses in vivo and silico ctDNA fragment length selection to enrich the variant proportion in the plasma. The method is decided on size selection criteria based on blood ctDNA fragment length properties, so it may not generalize well for other non-invasive sampling methods. Furthermore, it employs supervised machine learning methods like Random Forest and Logistic Regression on shallow WGS to classify cancer and healthy patients. The method can be used for different cancer types.