The averaged chromatograms for the bladder cancer group and the control group shown on the same standardised resistance time plot.

<p>The averaged chromatograms for the bladder cancer group and the control group shown on the same standardised resistance time plot.</p

Examples of chromatograms (standardised resistance vs. time) of the urine of two patients with bladder cancer and two controls.

<p>Examples of chromatograms (standardised resistance vs. time) of the urine of two patients with bladder cancer and two controls.</p

The retention time stability of three common peaks from 24 selected control samples and the 24 bladder cancer samples, in date order.

<p>There is a minimal day to day fluctuation <i>circa</i> ± 1%.</p

Accuracy results of repeated 10 fold cross validation of the Random Forest and Linear Discriminant Analysis (LDA) models built to classify urine samples from patients with prostate cancer and cancer-free controls based on the presence or absence of VOCs.

<p>Accuracy results of repeated 10 fold cross validation of the Random Forest and Linear Discriminant Analysis (LDA) models built to classify urine samples from patients with prostate cancer and cancer-free controls based on the presence or absence of VOCs.</p

Additional file 1: of The feasibility of the Prostate cancer: Evidence of Exercise and Nutrition Trial (PrEvENT) dietary and physical activity modifications: a qualitative study

Sample topic guide questions. A selection of sample interview questions used to collect the qualitative data. (DOCX 11 kb

The pipeline of the validation techniques known as repeated 10 fold cross validation and repeated double cross validation.

<p>A Monte-Carlo variation of each technique is achieved by randomising the labels of the testing samples.</p

Accuracy results of PSA testing for prostate cancer assessed with repeated 10 fold cross validation of Random Forest and Linear Discriminant Analysis (LDA) models.

<p>Accuracy results of PSA testing for prostate cancer assessed with repeated 10 fold cross validation of Random Forest and Linear Discriminant Analysis (LDA) models.</p

Approaches and R packages applied for feature selection prior to statistical modelling.

<p>Approaches and R packages applied for feature selection prior to statistical modelling.</p

Accuracy results of PSA testing for prostate cancer assessed using repeated double cross validation of Random Forest and Linear Discriminant Analysis (LDA) models.

<p>Accuracy results of PSA testing for prostate cancer assessed using repeated double cross validation of Random Forest and Linear Discriminant Analysis (LDA) models.</p

Receiver operating characteristic curve (ROC) for the random forest (RF) and linear discriminant analysis (LDA) models built using repeated double cross-validation to classify patients with prostate cancer and cancer-free controls based on PSA levels and VOCs in urine headspace.

<p>Receiver operating characteristic curve (ROC) for the random forest (RF) and linear discriminant analysis (LDA) models built using repeated double cross-validation to classify patients with prostate cancer and cancer-free controls based on PSA levels and VOCs in urine headspace.</p