A recurrence model for non-puerperal mastitis patients based on machine learning.

Journal: PloS one
PMID: 39820962

Abstract

OBJECTIVE: Non-puerperal mastitis (NPM) is an inflammatory breast disease affecting women during non-lactation periods, and it is prone to relapse after being cured. Accurate prediction of its recurrence is crucial for personalized adjuvant therapy, and pathological examination is the primary basis for the classification, diagnosis, and confirmation of non-puerperal mastitis. Currently, there is a lack of recurrence models for non-puerperal mastitis. The aim of this research is to create and validate a recurrence model using machine learning for patients with non-puerperal mastitis.

Authors

  • Gaosha Li
    Department of Clinical Laboratory, Affiliated Jinhua Hospital, Zhejiang University School of Medicine, Jinhua, China.
  • Qian Yu
    State Key Laboratory of Characteristic Chinese Medicine Resources in Southwest China, Chengdu University of Traditional Chinese Medicine, Chengdu, China.
  • Feng Dong
    School of Economics and Management, China University of Mining and Technology, Xuzhou 221116, China.
  • Zhaoxia Wu
    Department of Control Engineering, Northeastern University, Qinhuangdao Campus, Qinhuangdao 066001, China.
  • Xijing Fan
    Department of Clinical Laboratory, Affiliated Jinhua Hospital, Zhejiang University School of Medicine, Jinhua, China.
  • Lingling Zhang
    Department of Information Technology, Hunan Women's University, Changsha, Hunan 410002, PR China. Electronic address: linglingmath@gmail.com.
  • Ying Yu
    School of Chemistry and Environment, Guangzhou Key Laboratory of Analytical Chemistry for Biomedicine, South China Normal University, Guangzhou 510006, PR China. Electronic address: yuyhs@scnu.edu.cn.

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