Shanghai Jiao Tong University School of Medicine Reports Findings in Machine Lea rning (Optimizing thyroid AUS nodules malignancy prediction: a comprehensive stu dy of logistic regression and machine learning models)

上海交通大学医学院机器学习研究报告(优化甲状腺结节恶性预测:logistic回归和机器学习模型的综合研究)

Robotics & Machine Learning Daily News2024，Issue(Dec.2) ：75-75.

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Shanghai Jiao Tong University School of Medicine Reports Findings in Machine Lea rning (Optimizing thyroid AUS nodules malignancy prediction: a comprehensive stu dy of logistic regression and machine learning models)

上海交通大学医学院机器学习研究报告(优化甲状腺结节恶性预测:logistic回归和机器学习模型的综合研究)

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摘要

由一名新闻记者-机器人与机器学习日报的工作人员新闻编辑每日新闻-机器学习的新研究是一篇报道的主题。据新闻报道来自中华人民共和国上海的报道，由NewsRx Research记者报道“甲状腺结节的准确诊断与细胞学不确定，特别是异型性。”意义不确定的(AUS)类别，仍然具有挑战性。本研究旨在预测风险两种机器学习(ML)与三种常规logistic回归分析在AUS结节恶性程度回归(LR)模型"。

Abstract

By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News - New research on Machine Learning is th e subject of a report. According to newsreporting originating from Shanghai, Pe ople’s Republic of China, by NewsRx correspondents, researchstated, “The accura te diagnosis of thyroid nodules with indeterminate cytology, particularly in the atypiaof undetermined significance (AUS) category, remains challenging. This s tudy aims to predict the riskof malignancy in AUS nodules by comparing two mach ine learning (ML) and three conventional logisticregression (LR) models.”

Key words

Shanghai/People’s Republic of China/As ia/Cyborgs/Emerging Technologies/Machine Learning/Risk and Prevention

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出版年

2024

Robotics & Machine Learning Daily News

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