Predicting disease risks from highly imbalanced data using random forest.

ABSTRACT:

Background

We present a method utilizing Healthcare Cost and Utilization Project (HCUP) dataset for predicting disease risk of individuals based on their medical diagnosis history. The presented methodology may be incorporated in a variety of applications such as risk management, tailored health communication and decision support systems in healthcare.

Methods

We employed the National Inpatient Sample (NIS) data, which is publicly available through Healthcare Cost and Utilization Project (HCUP), to train random forest classifiers for disease prediction. Since the HCUP data is highly imbalanced, we employed an ensemble learning approach based on repeated random sub-sampling. This technique divides the training data into multiple sub-samples, while ensuring that each sub-sampl

SUBMITTER: Khalilia M 

PROVIDER: S-EPMC3163175 | biostudies-literature | 2011 Jul

REPOSITORIES: biostudies-literature