A random forest classifier predicts recurrence risk in patients with ovarian cancer-Reference-Cited by-同舟云学术

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A random forest classifier predicts recurrence risk in patients with ovarian cancer

Published:2018-07-19 Issue: Volume: Page:
ISSN:1791-2997
Container-title:Molecular Medicine Reports
language:
Short-container-title:Mol Med Report

Author:

Cheng Li¹,Li Lin¹,Wang Liling¹,Li Xiaofang¹,Xing Hui¹,Zhou Jinting¹

Affiliation:

1. Department of Obstetrics and Gynecology, Xiangyang Central Hospital (Affiliated Hospital of Hubei University of Arts and Science), Xiangyang, Hubei 441021, P.R. China

Publisher

Spandidos Publications

Subject

Cancer Research,Oncology,Genetics,Molecular Biology,Molecular Medicine,Biochemistry

Cited by 14 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Use of artificial intelligence for the prediction of lymph node metastases in early-stage colorectal cancer: systematic review;BJS Open;2024-03-01

2. Development and validation of a preoperative CT-based risk scoring system for predicting recurrence-free survival in patients undergoing curative surgery for gastric cancer;European Journal of Radiology;2024-02

3. Using machine learning models to predict synchronous genitourinary cancers among gastrointestinal stromal tumor patients;Urology Annals;2024-01

4. Ovarian Cancer Prediction using Machine Learning;2023 3rd International Conference on Smart Generation Computing, Communication and Networking (SMART GENCON);2023-12-29

5. A prognostic model for ovarian neoplasms established by an integrated analysis of 1580 transcriptomic profiles;Scientific Reports;2023-11-08

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