Enhancement of hepatitis virus immunoassay outcome predictions in imbalanced routine pathology data by data balancing and feature selection before the application of support vector machines.

Journal: BMC medical informatics and decision making

PMID: 28806936

Abstract

BACKGROUND: Data mining techniques such as support vector machines (SVMs) have been successfully used to predict outcomes for complex problems, including for human health. Much health data is imbalanced, with many more controls than positive cases.

Authors

Alice M Richardson

Present address: National Centre for Epidemiology & Population Health, Australian National University, Canberra, ACT 2601, Australia. alice.richardson@anu.edu.au.
Brett A Lidbury

Pattern Recognition and Pathology, John Curtin School of Medical Research, Australian National University, 62 Mills Rd, Acton, ACT 2601, Australia.

Keywords

Data Mining Hepatitis B Hepatitis C Humans Immunoassay Predictive Value of Tests Support Vector Machine

External Resources

View on PubMed Access via DOI PubMed (28806936)

Enhancement of hepatitis virus immunoassay outcome predictions in imbalanced routine pathology data by data balancing and feature selection before the application of support vector machines.

Abstract

Authors

Keywords

External Resources

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