A comparative study on feature selection for a risk prediction model for colorectal cancer

Background and objective
Risk prediction models aim at identifying people at higher risk of developing a target disease. Feature selection is particularly important to improve the prediction model performance avoiding overfitting and to identify the leading cancer risk (and protective) factors. Assessing the stability of feature selection/ranking algorithms becomes an important issue when the aim is to analyze the features with more prediction power. Methods
This work is focused on colorectal cancer, assessing several feature ranking algorithms in terms of …

aim cancer cs.ai cs.lg disease feature feature selection identify overfitting people performance prediction prediction models risk stability study