Biostatistics Commons^™

Using Methods From The Data-Mining And Machine-Learning Literature For Disease Classification And Prediction: A Case Study Examining Classification Of Heart Failure Subtypes, Peter C. Austin

Peter Austin

OBJECTIVE: Physicians classify patients into those with or without a specific disease. Furthermore, there is often interest in classifying patients according to disease etiology or subtype. Classification trees are frequently used to classify patients according to the presence or absence of a disease. However, classification trees can suffer from limited accuracy. In the data-mining and machine-learning literature, alternate classification schemes have been developed. These include bootstrap aggregation (bagging), boosting, random forests, and support vector machines.

STUDY DESIGN AND SETTING: We compared the performance of these classification methods with that of conventional classification trees to classify patients with heart failure (HF) …

Regression Trees For Predicting Mortality In Patients With Cardiovascular Disease: What Improvement Is Achieved By Using Ensemble-Based Methods?, Peter C. Austin

Peter Austin

In biomedical research, the logistic regression model is the most commonly used method for predicting the probability of a binary outcome. While many clinical researchers have expressed an enthusiasm for regression trees, this method may have limited accuracy for predicting health outcomes. We aimed to evaluate the improvement that is achieved by using ensemble-based methods, including bootstrap aggregation (bagging) of regression trees, random forests, and boosted regression trees. We analyzed 30-day mortality in two large cohorts of patients hospitalized with either acute myocardial infarction (N = 16,230) or congestive heart failure (N = 15,848) in two distinct eras (1991-2001 and …