Work

Improving machine learning performance by removing redundant cases in medical data sets.

Year: 1998

Type: article

Abstract: Neural network models and other machine learning methods have successfully been applied to several medical classification problems. These models can be periodically refined and retrained as new cases become available. Since training neural networks by backpropagation is time consuming, it is desirable that a minimum number of representative cases be kept in the training set (i.e., redundant cases should be removed). The removal of redundant cases should be carefully monitored so that classification performance is not significantly affected. We made experiments on data removal on a data set of 700 patients suspected of having myocardial infarction and show that there is no statistical difference in classification performance (measured by the differences in areas under the ROC curve on two previously unknown sets of 553 and 500 cases) when as many as 86% of the cases are randomly removed. A proportional reduction in the amount of time required to train the neural network model is achieved.

Source: PubMed Central

Authors: L Ohno-Machado, H S Fraser, A Ohrn

Institutions: Brigham and Women's Hospital, Harvard University

Language: English

Topic: Artificial Intelligence in Healthcare

Subfield: Health Information Management

Field: Health Professions

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: green

Funders: National Heart, Lung, and Blood Institute, U.S. National Library of Medicine

Awards: ARTIFICIAL INTELLIGENCE CARDIOVASCULAR REASONING, COMPONENT BASED TOOLS FOR CONNECTIONIST CLASSIFICATION