A Method to Extract Feature Variables Contributed in Nonlinear Machine Learning Prediction.

Journal: Methods of information in medicine
Published Date:

Abstract

BACKGROUND: Although advances in prediction accuracy have been made with new machine learning methods, such as support vector machines and deep neural networks, these methods make nonlinear machine learning models and thus lack the ability to explain the basis of their predictions. Improving their explanatory capabilities would increase the reliability of their predictions.

Authors

  • Mayumi Suzuki
    Hitachi, Ltd. Research and Development Group, Tokyo, Japan.
  • Takuma Shibahara
    All authors: Institute of Advanced Biomedical Engineering and Science, Tokyo Women's Medical University, Tokyo, Japan.
  • Yoshihiro Muragaki
    All authors: Institute of Advanced Biomedical Engineering and Science, Tokyo Women's Medical University, Tokyo, Japan.