Machine Learning Approach for Intraocular Disease Prediction Based on Aqueous Humor Immune Mediator Profiles.

Journal: Ophthalmology
PMID: 33484732

Abstract

PURPOSE: Various immune mediators have crucial roles in the pathogenesis of intraocular diseases. Machine learning can be used to automatically select and weigh various predictors to develop models maximizing predictive power. However, these techniques have not yet been applied extensively in studies focused on intraocular diseases. We evaluated whether 5 machine learning algorithms applied to the data of immune-mediator levels in aqueous humor can predict the actual diagnoses of 17 selected intraocular diseases and identified which immune mediators drive the predictive power of a machine learning model.

Authors

  • Naoya Nezu
    Department of Ophthalmology, Tokyo Medical University Hospital, Tokyo, Japan.
  • Yoshihiko Usui
    Department of Ophthalmology, Tokyo Medical University Hospital, Tokyo, Japan. Electronic address: usuyoshi@gmail.com.
  • Akira Saito
    Division of Life Science and Engineering, School of Science and Engineering, Tokyo Denki University (TDU), Ishizaka, Hatoyama-Machi, Hiki-Gun, Saitama, 350-0394, Japan.
  • Hiroyuki Shimizu
    Department of Ophthalmology, Tokyo Medical University Hospital, Tokyo, Japan.
  • Masaki Asakage
    Department of Ophthalmology, Tokyo Medical University Hospital, Tokyo, Japan.
  • Naoyuki Yamakawa
    Department of Ophthalmology, Tokyo Medical University Hospital, Tokyo, Japan.
  • Kinya Tsubota
    Department of Ophthalmology, Tokyo Medical University Hospital, Tokyo, Japan.
  • Yoshihiro Wakabayashi
    Department of Ophthalmology, Tokyo Medical University Hospital, Tokyo, Japan.
  • Akitomo Narimatsu
    Department of Ophthalmology, Tokyo Medical University Hospital, Tokyo, Japan.
  • Kazuhiko Umazume
    Department of Ophthalmology, Tokyo Medical University Hospital, Tokyo, Japan.
  • Katsuhiko Maruyama
    Department of Ophthalmology, Tokyo Medical University Hospital, Tokyo, Japan.
  • Masahiro Sugimoto
    Institute for Advanced Biosciences, Keio University, Tsuruoka, Yamagata 997-0052, Japan. mshrsgmt@tokyo-med.ac.jp.
  • Masahiko Kuroda
  • Hiroshi Goto

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