Additional file 1 of Artificial intelligence for the prevention and prediction of colorectal neoplasms
Creators: name: Tokutake, Kohjiro
name: Morelos-Gomez, Aaron
name: Hoshi, Ken-ichi
name: Katouda, Michio
name: Tejima, Syogo
name: Endo, Morinobu
Publisher: name: figshare
Year: 2023
Subjects: subject: Medicine
subject: Biotechnology
classificationCode: 39999
subject: Chemical Sciences not elsewhere classified
subject: Ecology
subject: FOS: Biological sciences
subject: Sociology
subject: FOS: Sociology
classificationCode: 69999
subject: Biological Sciences not elsewhere classified
classificationCode: 80699
subject: Information Systems not elsewhere classified
classificationCode: 19999
subject: Mathematical Sciences not elsewhere classified
subject: Cancer

description: Additional file 1: Table S1. Purpose of colonoscopy. Table S2. Past history of all cases. Table S3. Statistical description of men data distributions for each feature. Table S4. Statistical description of women data distributions for each feature. Figure S1. Examples of KDE plots that are (a) less and (b) more alike among patients with (yes) and without (no) polyps, for the men’s dataset. Table S5. Direction of highest KDE values. KDE is 1 (representing the highest probability to have polyp) when the feature value is lower or higher than the maximum KDE point. Table S6. Optimal model performance for men test data after training on men data at different polyp size threshold and KDE exponent, for KDE transformation (normal) and sigmoid-like KDE transformation (sigmoid). NA stands for not applied, without KDE transformation. The classifiers are adaptive boosting (AdaBoost), Gaussian process, gradient boost, linear discriminant analysis (LDA), linear support vector classifier (Linear SVC), logistic regression, multilayer perceptron (MLP), ridge classifier (Ridge) and support vector classifier (SVC). MCC is the Matthews correlation coefficient. Table S7. Optimal model performance for men test data after training at different polyp size threshold without KDE transformation. The classifiers are adaptive boosting (AdaBoost), Gaussian process, gradient boost, linear discriminant analysis (LDA), linear support vector classifier (Linear SVC), logistic regression, multilayer perceptron (MLP), ridge classifier (Ridge) and support vector classifier (SVC). MCC is the Matthews correlation coefficient. Table S8. Optimal model performance for women test data at different polyp size threshold and KDE exponent, for KDE transformation (normal) and sigmoid-like transformation (sigmoid). NA stands for not applied, without KDE transformation. The classifiers are adaptive boosting (AdaBoost), Bernoulli Naïve Bayes (BernoulliNB), Gaussian process, linear discriminant analysis (LDA), linear support vector classifier (Linear SVC), logistic regression, multilayer perceptron (MLP), ridge classifier (ridge) and support vector classifier (SVC). MCC is the Matthews correlation coefficient. Table S9. Optimal model performance for women test data after training at different polyp size threshold without KDE transformation. The classifiers are adaptive boosting (AdaBoost), Gaussian process, gradient boost, linear discriminant analysis (LDA), linear support vector classifier (Linear SVC), logistic regression, multilayer perceptron (MLP), ridge classifier (Ridge) and support vector classifier (SVC). MCC is the Matthews correlation coefficient. Table S10. Percentage of patients with polyps per drinking habit. For drinking frequency, the subgroups were no drinking, sometimes, and everyday. For drinking mass, the subgroups were 3 cans/day. Table S11. Kruskal-Wallis H-test results for polyp and gender as independent variables, data is shown for H and P values. Table S12. Upper limits to discard data from each feature.