Skip to main content

Prediction of 30-Day Readmission: An Improved Gradient Boosting Decision Tree Approach

Buy Article:

$107.14 + tax (Refund Policy)

The rate of 30-day hospital readmission is commonly used to measure the quality of medical service. To improve the hospital efficiency, in this study, we have proposed a balanced fuzzy C-Means LightGBM classifier (BFCMLGB) to predict the 30-day patient readmission using a published readmission dataset. The results show that the BFCM-LGB algorithm can effectively process the problem of the imbalanced readmission data, and further conduct a feature extraction and a parameter optimization. Compared with other known algorithms, BFCM-LGB performs the best, in terms of four measurements, accuracy, G-mean, F-score, and area under the curve. Altogether, BFCM-LGB can be a useful classification algorithm to predict 30-day patient readmission.

Keywords: 30-DAY READMISSION; FEATURE EXTRACTION; FUZZY C-MEANS; GRADIENT BOOSTING DECISION TREE; IMBALANCED DATA

Document Type: Research Article

Publication date: March 1, 2019

More about this publication?
  • Journal of Medical Imaging and Health Informatics (JMIHI) is a medium to disseminate novel experimental and theoretical research results in the field of biomedicine, biology, clinical, rehabilitation engineering, medical image processing, bio-computing, D2H2, and other health related areas.
  • Editorial Board
  • Information for Authors
  • Subscribe to this Title
  • Ingenta Connect is not responsible for the content or availability of external websites
  • Access Key
  • Free content
  • Partial Free content
  • New content
  • Open access content
  • Partial Open access content
  • Subscribed content
  • Partial Subscribed content
  • Free trial content