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Output details

11 - Computer Science and Informatics

Robert Gordon University

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Output 48 of 72 in the submission

Article title

Machine learning for improved pathological staging of prostate cancer: A performance comparison on a range of classifiers

Type

D - Journal article

DOI

10.1016/j.artmed.2011.11.003

Title of journal

Artificial Intelligence in Medicine

Article number

Volume number

Issue number

First page of article

ISSN of journal

09333657

Year of publication

2012

URL

Number of additional authors

Additional information

This is the first application of Bayesian Networks (BN) to prostate cancer stage prediction and the first to replicate Partin's work on the authoritative BAUS UK dataset. Partin tables are used globally so this is significant to urologists. This output was discussed in a recent article on tools for prostate cancer (Nature Reviews Urology doi:10.1038/nrurol.2013.9) and cited as an important example of probabilistic risk assessment in disease diagnosis and management . The BN method outperformed the gold-standard logistic regression technique and many other machine learning techniques.

A related project won ScotlandIS Young Software Engineer of the Year 2012 (http://www.scotlandis.com/news/stories/software-students-collect-a-clutch-of-awards)

Interdisciplinary

Cross-referral requested

Research group

None

Citation count

Proposed double-weighted

Double-weighted statement

Reserve for a double-weighted output

Non-English

English abstract