New Risk Modeling Method for Robust Learning on Smaller Samples

Prognosis of disease progression is necessary for development of individualized treatment, understanding of the disease. Risk modeling is a challenging problem, and too often amount of available relevant observations is not sufficient to build a quality model with traditional approaches. New method Smooth Rank for survival analysis, risk modeling is introduced here. Smooth Rank is robust against overfitting on relatively small samples. The method is compared with established risk modeling methods on 10 real life datasets. The experiments confirmed significant advantage of the proposed method on smaller samples.