APA (7th ed.) Citation

Li, D., Chang, B., & Huang, Q. Using XBGoost, an interpretable machine learning model, for diagnosing prostate cancer in patients with PSA < 20 ng/ml based on the PSAMR indicator. Nature Portfolio.

Chicago Style (17th ed.) Citation

Li, Dengke, Baoyuan Chang, and Qunlian Huang. Using XBGoost, an Interpretable Machine Learning Model, for Diagnosing Prostate Cancer in Patients with PSA < 20 Ng/ml Based on the PSAMR Indicator. Nature Portfolio.

MLA (9th ed.) Citation

Li, Dengke, et al. Using XBGoost, an Interpretable Machine Learning Model, for Diagnosing Prostate Cancer in Patients with PSA < 20 Ng/ml Based on the PSAMR Indicator. Nature Portfolio.