1. A method for selecting features for a final prediction rule predictive of an outcome with respect to a medical condition, said method comprising: performing with a computer-implemented machine learning tool: (a) generating a prediction rule based on training data for a cohort of patients whose outcomes with respect to said medical condition are at least partially known, wherein for each patient the data comprises measurements for a set of features and the outcome with respect to said medical condition for said patient to the extent known, wherein in a first iteration of (a) said set of features includes n features with n greater than or equal to 3 with n being decremented by one in each subsequent iteration of (a); (b) determining a fitness value for said prediction rule, wherein said determining a fitness value comprises summing a concordance index (CI) of said prediction rule with a product of a sensitivity and a specificity of said prediction rule; (c) determining a value of contribution to said prediction rule for each of said features in said set of features; (d) removing a feature from consideration from said set of features based on the values of contribution, wherein the feature having the lowest value of contribution is removed; (e) iterating (a)-(d) in order to produce n prediction rules and n fitness values; and (f) selecting, based on the fitness values for said n prediction rules, one of said n prediction rules as said final prediction rule predictive of the outcome with respect to said medical condition, wherein of said n prediction rules said final prediction rule has the highest predictive ability with respect to the outcome with respect to said medical condition as indicated by said fitness values; and evaluating data for a patient with a computer implementation of said final prediction rule to produce a value predictive of the patient's outcome with respect to said medical condition.