A stacking ensemble model for predicting the occurrence of carotid atherosclerosis.

Journal: Frontiers in endocrinology
PMID: 39109079

Abstract

BACKGROUND: Carotid atherosclerosis (CAS) is a significant risk factor for cardio-cerebrovascular events. The objective of this study is to employ stacking ensemble machine learning techniques to enhance the prediction of CAS occurrence, incorporating a wide range of predictors, including endocrine-related markers.

Authors

  • Xiaoshuai Zhang
    Department of Epidemiology and Biostatistics, School of Public Health, Shandong University, PO Box 100, Jinan, 250012, China.
  • Chuanping Tang
    Department of Data Science, School of Statistics and Mathematics, Shandong University of Finance and Economics, Jinan, China.
  • Shuohuan Wang
    Information Technology Division, Shandong International Trust Co., Ltd., Jinan, China.
  • Wei Liu
    Department of Radiation Oncology, Mayo Clinic, Scottsdale, AZ, United States.
  • Wangxuan Yang
    School of Public Health, Harbin Medical University, Harbin, China.
  • Di Wang
    Center for Endocrine Metabolism and Immune Diseases, Beijing Luhe Hospital, Capital Medical University, Beijing, People's Republic of China.
  • Qinghuan Wang
    Department of Data Science, School of Statistics and Mathematics, Shandong University of Finance and Economics, Jinan, China.
  • Fang Tang

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