Predicting peripartum depression using elastic net regression and machine learning: the role of remnant cholesterol.

Journal: BMC pregnancy and childbirth
PMID: 40340559

Abstract

BACKGROUND: Traditional statistical methods have dominated research on peripartum depression (PPD), but innovative approaches may provide deeper insights. This study aims to predict the impact factors of PPD using elastic net regression (ENR) combined with machine learning (ML) model.

Authors

  • Hongxu Chen
    School of Public Health, Xinjiang Medical University, Urumqi, 830063, China.
  • Denglan Wang
    Xinjiang Key Laboratory of Neurological Disorder Research, the Second Affiliated Hospital of Xinjiang Medical University, Urumqi, 830063, China.
  • Juanjuan Shen
    Xinjiang Key Laboratory of Neurological Disorder Research, the Second Affiliated Hospital of Xinjiang Medical University, Urumqi, 830063, China.
  • Baoyan Guo
    Xinjiang Key Laboratory of Neurological Disorder Research, the Second Affiliated Hospital of Xinjiang Medical University, Urumqi, 830063, China.
  • Chun Song
    Xinjiang Key Laboratory of Neurological Disorder Research, the Second Affiliated Hospital of Xinjiang Medical University, Urumqi, 830063, China.
  • Duo Ma
    Department of Ultrasonography, The Second Afffliated Hospital of Xiamen Medical College, Xiamen, China.
  • Yan Wu
    Beijing Hui-Long-Guan Hospital, Peking University, Beijing, 100096, China.
  • Guohui Liu
    Inner Mongolia Maternity and Child Health Care Hospital, Huhhot, 010020, China.
  • Guangxue Chen
    Department of Gynaecology and Obstetrics, Beijing Jishuitan Hospital, Capital Medical University, Beijing, 100035, China.
  • Yan Ni
    Department of Women Health Care, Quzhou Maternal and Child Health Care Hospital, Quzhou, 324000, China.
  • Tiantian Kong
    Xinjiang Key Laboratory of Neurological Disorder Research, the Second Affiliated Hospital of Xinjiang Medical University, Urumqi, 830063, China. 123457417@qq.com.
  • Fan Wang
    Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming, China.

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