Application of Random Forest Algorithm in Bio Informatics

Abstract

This chapter aims at briefing random forest algorithms in the healthcare domain. Feature extraction and relationships are incorporated into a classification model, which is a discriminant function training algorithm. Once the user understands how random forests work, the user can quickly implement them in their data. They also produce predictions with high reliability when used in groups. It is briefly discussed in this work how classification tree-based strategies are used. The current advances and their applicability in genomics and statistics genetics are also included here.