Development and validation of colorectal cancer risk prediction tools: A comparison of models

[1]  R. Hayes,et al.  Risk-stratified screening for colorectal cancer using genetic and environmental risk factors: A cost-effectiveness analysis based on real-world data. , 2023, Clinical gastroenterology and hepatology : the official clinical practice journal of the American Gastroenterological Association.

[2]  I. Nagtegaal,et al.  Personalized colorectal cancer screening: study protocol of a mixed-methods study on the effectiveness of tailored intervals based on prior f-Hb concentration in a fit-based colorectal cancer screening program (PERFECT-FIT) , 2023, BMC Gastroenterology.

[3]  Rasheed Omobolaji Alabi,et al.  An interpretable machine learning prognostic system for risk stratification in oropharyngeal cancer , 2022, Int. J. Medical Informatics.

[4]  B. van Calster,et al.  Interpreting area under the receiver operating characteristic curve , 2022, The Lancet Digital Health.

[5]  E. Kuipers,et al.  Faecal occult blood loss accurately predicts future detection of colorectal cancer. A prognostic model , 2022, Gut.

[6]  Nicholson T. Collier,et al.  An Evolutionary Algorithm to Personalize Stool-Based Colorectal Cancer Screening , 2022, Frontiers in Physiology.

[7]  H. Sørensen,et al.  Risk-stratified selection to colonoscopy in FIT colorectal cancer screening: development and temporal validation of a prediction model , 2022, British Journal of Cancer.

[8]  Brian D. Williamson,et al.  Improving random forest predictions in small datasets from two-phase sampling designs , 2021, BMC Medical Informatics and Decision Making.

[9]  C. Exbrayat,et al.  Prediction of the severity of colorectal lesion by fecal hemoglobin concentration observed during previous test in the French screening program , 2021, World journal of gastroenterology.

[10]  Evert de Jonge,et al.  Machine learning for developing a prediction model of hospital admission of emergency department patients: Hype or hope? , 2021, Int. J. Medical Informatics.

[11]  Benjamin S. Aribisala,et al.  Breast cancer risk prediction in African women using Random Forest Classifier. , 2021, Cancer treatment and research communications.

[12]  Xuan Song,et al.  Comparison of machine learning and logistic regression models in predicting acute kidney injury: A systematic review and meta-analysis , 2021, Int. J. Medical Informatics.

[13]  H. Brenner,et al.  Effect of Sex, Age and Positivity Threshold on Fecal Immunochemical Test Accuracy: a Systematic Review and Meta-Analysis. , 2019, Gastroenterology.

[14]  M. Jenkins,et al.  Cost-Effectiveness of Personalized Screening for Colorectal Cancer Based on Polygenic Risk and Family History , 2019, Cancer Epidemiology, Biomarkers & Prevention.

[15]  X. Castells,et al.  Changes in FIT values below the threshold of positivity and short-term risk of advanced colorectal neoplasia: Results from a population-based cancer screening program. , 2019, European journal of cancer.

[16]  A. Boulesteix,et al.  Random forest versus logistic regression: a large-scale benchmark experiment , 2018, BMC Bioinform..

[17]  Philipp Probst,et al.  Hyperparameters and tuning strategies for random forest , 2018, WIREs Data Mining Knowl. Discov..

[18]  A. Barkun,et al.  Systematic review of colorectal cancer screening guidelines for average-risk adults: Summarizing the current global recommendations , 2018, World journal of gastroenterology.

[19]  E. Kuipers,et al.  Real-Time Monitoring of Results During First Year of Dutch Colorectal Cancer Screening Program and Optimization by Altering Fecal Immunochemical Test Cut-Off Levels. , 2017, Gastroenterology.

[20]  E. Kuipers,et al.  Adherence to colorectal cancer screening: four rounds of faecal immunochemical test-based screening , 2016, British Journal of Cancer.

[21]  S. Chiou,et al.  Faecal haemoglobin concentration influences risk prediction of interval cancers resulting from inadequate colonoscopy quality: analysis of the Taiwanese Nationwide Colorectal Cancer Screening Program , 2015, Gut.

[22]  E. Kuipers,et al.  Colorectal cancer screening: a global overview of existing programmes , 2015, Gut.

[23]  Hsiu-Hsi Chen,et al.  A new insight into fecal hemoglobin concentration‐dependent predictor for colorectal neoplasia , 2014, International journal of cancer.

[24]  D. V. Poel,et al.  Random Forests for multiclass classification: Random MultiNomial Logit , 2008, Expert Syst. Appl..

[25]  Tom Fawcett,et al.  An introduction to ROC analysis , 2006, Pattern Recognit. Lett..

[26]  L. Breiman.  Random Forests , 2001, Encyclopedia of Machine Learning and Data Mining.

[27]  J. Ross Quinlan,et al.  Induction of Decision Trees , 1986, Machine Learning.

[28]  Kaitlin Kirasich,et al.  Random Forest vs Logistic Regression: Binary Classification for Heterogeneous Datasets , 2018 .