title: Prediction of hospitalization due to heart diseases by supervised learning methods.
abstract: BACKGROUND
  In 2008, the United States spent $2.2 trillion for healthcare, which was 15.5% of its GDP. 31% of this expenditure is attributed to hospital care. Evidently, even modest reductions in hospital care costs matter. A 2009 study showed that nearly $30.8 billion in hospital care cost during 2006 was potentially preventable, with heart diseases being responsible for about 31% of that amount.
  
  
  METHODS
  Our goal is to accurately and efficiently predict heart-related hospitalizations based on the available patient-specific medical history. To the best of our knowledge, the approaches we introduce are novel for this problem. The prediction of hospitalization is formulated as a supervised classification problem. We use de-identified Electronic Health Record (EHR) data from a large urban hospital in Boston to identify patients with heart diseases. Patients are labeled and randomly partitioned into a training and a test set. We apply five machine learning algorithms, namely Support Vector Machines (SVM), AdaBoost using trees as the weak learner, logistic regression, a naïve Bayes event classifier, and a variation of a Likelihood Ratio Test adapted to the specific problem. Each model is trained on the training set and then tested on the test set.
  
  
  RESULTS
  All five models show consistent results, which could, to some extent, indicate the limit of the achievable prediction accuracy. Our results show that with under 30% false alarm rate, the detection rate could be as high as 82%. These accuracy rates translate to a considerable amount of potential savings, if used in practice.
authors.affiliation: Department of Electrical & Computer Engineering, and Division of Systems Engineering, Boston University, 8 Saint Mary's Street, Boston, MA 02215, United States.
authors.forename: Wuyang
authors.initials: W
authors.lastname: Dai
authors.affiliation: Department of Electrical & Computer Engineering, and Division of Systems Engineering, Boston University, 8 Saint Mary's Street, Boston, MA 02215, United States.
authors.forename: Theodora S
authors.initials: TS
authors.lastname: Brisimi
authors.affiliation: Department of Pediatrics, Boston University School of Medicine and Boston Medical Center, 88 East Concord Street, Boston, MA 02118, United States.
authors.forename: William G
authors.initials: WG
authors.lastname: Adams
authors.affiliation: Electrophysiology Lab/Arrhythmia Service, Massachusetts General Hospital, 55 Fruit Street, Boston, MA 02114, United States.
authors.forename: Theofanie
authors.initials: T
authors.lastname: Mela
authors.affiliation: Department of Electrical & Computer Engineering, and Division of Systems Engineering, Boston University, 8 Saint Mary's Street, Boston, MA 02215, United States.
authors.forename: Venkatesh
authors.initials: V
authors.lastname: Saligrama
authors.affiliation: Department of Electrical & Computer Engineering, and Division of Systems Engineering, Boston University, 8 Saint Mary's Street, Boston, MA 02215, United States. Electronic address: yannisp@bu.edu.
authors.forename: Ioannis Ch
authors.initials: ICh
authors.lastname: Paschalidis
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D001499:Bayes Theorem; D001900:Boston; D057286:Electronic Health Records; D006331:Heart Diseases; D006760:Hospitalization; D006801:Humans; D016013:Likelihood Functions; D016015:Logistic Models; D012372:ROC Curve; D018570:Risk Assessment / Q000379:methods*
keywords: Electronic Health Records (EHRs); Heart diseases; Hospitalization; Machine learning; Predictive models; Prevention