A Random Forest Model Building Using A priori Information for Diagnosis

The problem of inductive model building on precedents for biomedical applications is considered. The model paradigm is a random forest as a set of decision tree classifiers working as ensemble. The apriori information taken from training data set is used in proposed method of random forest model building provide more accurate model saving general random character of a method. The resulting random forest provide more accurate model in comparison with a single decision tree, but its comparison with known methods of random forest model building proposed method is more accurate.

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