Ensemble of Classifiers for Length of Stay Prediction in Colorectal Cancer
The paper puts forward an ensemble of state-of-the-art classifiers – support vector machines, neural networks and decision trees – to estimate the length of stay after surgery in patients diagnosed with colorectal cancer. The three paradigms are brought together in order to achieve both a more accurate prediction through a voting scheme and transparency of the discriminative guidelines through visual rules. The results support the theoretical assumptions and are confirmed by the physicians.
KeywordsClassification Length of stay Colorectal cancer Ensemble of methods Prediction accuracy Decision guidelines
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