Extended Data Fig. 7: Machine learning model.

a, We used gradient tree boosting machine learning to examine whether cfDNA can be categorized as having characteristics of a patient with cancer or a healthy individual. The machine learning model included fragmentation size and coverage characteristics in windows throughout the genome, as well as chromosomal arm and mitochondrial DNA copy numbers. We used a tenfold cross-validation approach in which each sample is randomly assigned to a fold, and nine of the folds (90% of the data) are used for training and one fold (10% of the data) is used for testing. The prediction accuracy from a single cross-validation is an average over the ten possible combinations of test and training sets. As this prediction accuracy can reflect bias from the initial randomization of patients, we repeat the entire procedure, including the randomization of patients to folds, ten times. For all cases, feature selection and model estimation were performed on training data and were validated on test data, and the test data were never used for feature selection. Ultimately, we obtained a DELFI score that could be used to classify individuals as likely to be healthy or having cancer. b, Distribution of AUCs across the repeated tenfold cross-validation. The 25th, 50th and 75th percentiles of the 100 AUCs for the cohort of 215 healthy individuals and 208 patients with cancer are indicated by dashed lines.