Document Type
Article
Publication Date
6-15-2017
Department
Computing
School
Computing Sciences and Computer Engineering
Abstract
It is important to identify and prevent disease risk as early as possible through regular physical examinations. We formulate the disease risk prediction into a multilabel classification problem. A novel Ensemble Label Power-set Pruned datasets Joint Decomposition (ELPPJD) method is proposed in this work. First, we transform the multilabel classification into a multiclass classification. Then, we propose the pruned datasets and joint decomposition methods to deal with the imbalance learning problem. Two strategies size balanced (SB) and label similarity (LS) are designed to decompose the training dataset. In the experiments, the dataset is from the real physical examination records. We contrast the performance of the ELPPJD method with two different decomposition strategies. Moreover, the comparison between ELPPJD and the classic multilabel classification methods RAkEL and HOMER is carried out. The experimental results show that the ELPPJD method with label similarity strategy has outstanding performance.
Publication Title
Journal of Healthcare Engineering
Volume
2017
Recommended Citation
Li, R.,
Liu, W.,
Lin, Y.,
Zhao, H.,
Zhang, C.
(2017). An Ensemble Multilabel Classification for Disease Risk Prediction. Journal of Healthcare Engineering, 2017.
Available at: https://aquila.usm.edu/fac_pubs/15387
Comments
Published by 'Journal of Healthcare Engineering' at 10.1155/2017/8051673.