title: 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-sample is fully balanced. We compared the performance of support vector machine (SVM), bagging, boosting and RF to predict the risk of eight chronic diseases.
  
  
  RESULTS
  We predicted eight disease categories. Overall, the RF ensemble learning method outperformed SVM, bagging and boosting in terms of the area under the receiver operating characteristic (ROC) curve (AUC). In addition, RF has the advantage of computing the importance of each variable in the classification process.
  
  
  CONCLUSIONS
  In combining repeated random sub-sampling with RF, we were able to overcome the class imbalance problem and achieve promising results. Using the national HCUP data set, we predicted eight disease categories with an average AUC of 88.79%.
authors.affiliation: Department of Computer Science, University of Missouri, Columbia, Missouri, USA.
authors.forename: Mohammed
authors.initials: M
authors.lastname: Khalilia
authors.forename: Sounak
authors.initials: S
authors.lastname: Chakraborty
authors.forename: Mihail
authors.initials: M
authors.lastname: Popescu
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D019295:Computational Biology / Q000379:methods*; D016208:Databases, Factual; D004198:Disease Susceptibility / Q000453:epidemiology*; D058015:Health Communication; D006801:Humans; D016347:Medical Records Systems, Computerized / Q000706:statistics & numerical data; D018570:Risk Assessment