Identification of Risk Group for Root Caries and Analysis of Associated Factors in Older Adults Using Unsupervised Machine Learning Clustering.

Journal: Clinical interventions in aging
PMID: 40297169

Abstract

PURPOSE: This study aimed to identify the high-risk group for root caries using unsupervised machine learning and to explore the associated factors.

Authors

  • Linxin Jiang
    Stomatological Hospital, School of Stomatology, Southern Medical University, Guangzhou, Guangdong, People's Republic of China.
  • Shaohong Huang
    Department of Cardio-Thoracic Surgery, The Third Affiliated Hospital of Sun Yat-Sen University, Guangzhou, Guangdong, China.
  • Daniel R Reissmann
    Department of Prosthodontics, University of Leipzig, Leipzig, Germany.
  • Gerhard Schmalz
    Department of Conservative Dentistry and Periodontology, Brandenburg Medical School Theodor Fontane (MHB), Brandenburg/Havel, Germany.
  • Jianbo Li
    Department of Forensic Medicine, Faculty of Basic Medical Sciences, Chongqing Medical University, Chongqing 400016, China. Electronic address: 100390@cqmu.edu.cn.

Keywords

External Resources

Popular Topics
Recent Journals