Finding Risk Groups by Optimizing Artificial Neural Networks on the Area under the Survival Curve Using Genetic Algorithms

PloS One
Jonas KalderstamMattias Ohlsson

Abstract

We investigate a new method to place patients into risk groups in censored survival data. Properties such as median survival time, and end survival rate, are implicitly improved by optimizing the area under the survival curve. Artificial neural networks (ANN) are trained to either maximize or minimize this area using a genetic algorithm, and combined into an ensemble to predict one of low, intermediate, or high risk groups. Estimated patient risk can influence treatment choices, and is important for study stratification. A common approach is to sort the patients according to a prognostic index and then group them along the quartile limits. The Cox proportional hazards model (Cox) is one example of this approach. Another method of doing risk grouping is recursive partitioning (Rpart), which constructs a decision tree where each branch point maximizes the statistical separation between the groups. ANN, Cox, and Rpart are compared on five publicly available data sets with varying properties. Cross-validation, as well as separate test sets, are used to validate the models. Results on the test sets show comparable performance, except for the smallest data set where Rpart's predicted risk groups turn out to be inverted, an example of...Continue Reading

References

Feb 8, 1990·The New England Journal of Medicine·C G MoertelJ H Glick
May 14, 1982·JAMA : the Journal of the American Medical Association·F E HarrellR A Rosati
Apr 18, 2002·Neural Networks : the Official Journal of the International Neural Network Society·P J G Lisboa
Feb 18, 2006·Neural Networks : the Official Journal of the International Neural Network Society·Paulo J Lisboa, Azzam F G Taktak
Jun 13, 2006·Neural Computation·Geoffrey E HintonYee-Whye Teh
Apr 16, 2013·Artificial Intelligence in Medicine·Jonas KalderstamMattias Ohlsson

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Rpart
R
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nwtco
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survival

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