Prediction models for postoperative recurrence of non-lactating mastitis based on machine learning.

Journal: BMC medical informatics and decision making
PMID: 38649879

Abstract

OBJECTIVES: This study aims to build a machine learning (ML) model to predict the recurrence probability for postoperative non-lactating mastitis (NLM) by Random Forest (RF) and XGBoost algorithms. It can provide the ability to identify the risk of NLM recurrence and guidance in clinical treatment plan.

Authors

  • Jiaye Sun
    Department of Mammary, Shuguang Hospital, Shanghai University of Traditional Chinese Medicine, 200021, Shanghai, China.
  • Shijun Shao
    CAS Key Laboratory of Chemistry of Northwestern Plant Resources and Key Laboratory for Natural Medicine of Gansu Province, Lanzhou Institute of Chemical Physics, Chinese Academy of Sciences, Lanzhou, Gansu, 730000, PR China. Electronic address: sjshao@licp.cas.cn.
  • Hua Wan
    College of Mathematics and Informatics, South China Agricultural University, Guangzhou 510106, China.
  • Xueqing Wu
    Department of Mammary, Shuguang Hospital, Shanghai University of Traditional Chinese Medicine, 200021, Shanghai, China. snow_zi@hotmail.com.
  • Jiamei Feng
    Department of Mammary, Shuguang Hospital, Shanghai University of Traditional Chinese Medicine, 200021, Shanghai, China.
  • Qingqian Gao
    Department of Mammary, Shuguang Hospital, Shanghai University of Traditional Chinese Medicine, 200021, Shanghai, China.
  • Wenchao Qu
    Department of Mammary, Shuguang Hospital, Shanghai University of Traditional Chinese Medicine, 200021, Shanghai, China.
  • Lu Xie
    Shanghai Center for Bioinformation Technology, Shanghai Academy of Science and Technology, 1278 Keyuan Road, Shanghai 201203, PR China. Electronic address: xielu@scbit.org.

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