Ensemble of Classifiers for Length of Stay Prediction in Colorectal Cancer

摘要

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.