Conference proceeding
Improving risk grouping rules for prostate cancer patients using self-organising maps
DESIGN AND APPLICATION OF HYBRID INTELLIGENT SYSTEMS, Vol.104
Frontiers in Artificial Intelligence and Applications
01 Jan 2003
Abstract
Accurate classification of prostate cancer patients into risk groups is important to assist in the identification of appropriate treatment paths. Current rules used to classify patients into low, intermediate and high risk groups have been developed by clinical experts using an evidence based approach. The available data from the Alfred Hospital is quite limited however, since records exist for only 258 patients that have been treated and followed up over a five-year period. Consequently, a data-driven rule generation approach is seen as inappropriate for such a limited sample size. Instead, we start with the existing rules and aim to improve accuracy by identifying inconsistencies utilising self-organising maps as a data visualisation tool. The improved classification rules have been able to increase both the quality of prediction and the homogeneity within the risk groups.
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Details
- Title
- Improving risk grouping rules for prostate cancer patients using self-organising maps
- Creators
- D SchwarzK A SmithL ChurilovM DallyR Weber
- Contributors
- A Abraham (Editor)M Koppen (Editor)K Franke (Editor)
- Publication Details
- DESIGN AND APPLICATION OF HYBRID INTELLIGENT SYSTEMS, Vol.104
- Series
- Frontiers in Artificial Intelligence and Applications
- Publisher
- Ios Press
- Number of pages
- 10
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- Information Science (Informatics); School of Education
- Identifiers
- 991019238782004721
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