Decision Tree in Biology

Abstract

Purpose: Human biology is an essential field in scientific research as it helps in understanding the human body for adequate care. Technology has improved the way scientists do their biological research. One of the critical technologies is artificial intelligence (AI), which is revolutionizing the world. Scientists have applied AI in biological studies, using several methods to gain different types of data. Machine learning is a branch of artificial intelligence that helps computers learn from data and create predictions without being explicitly programmed. Methodology: One critical methodology in the machine is using the tree-based decision. It is extensively used in biological research, as it helps in classifying complex data into simple and easy to interpret graphs. This paper aims to give a beginner-friendly view of the tree-based model, analyzing its use and advantages over other methods. Finding: Artificial intelligence has greatly improved the collection, analysis, and prediction of biological and medical information. Machine learning, a subgroup of artificial intelligence, is useful in creating prediction models, which help a wide range of fields, including computational and systems biology. Contribution and future recommendation also discussed in this study.