A Case-Based Reasoning System-Based Random Forest for Classification

Abstract

The huge amount of health data attracts machine learning (ML) techniques to medical classification, and, through learning strategies, obtain remarkable results. Some techniques are used to classify and predict data to make accurate decisions, especially case-based reasoning (CBR), which is considered a reasonable technique in medicine, based on past experiences for problem solving. This chapter studies the case-based reasoning approach and its use in the medical field. In the analysis, the authors identify hybridization as a major trend in CBR. Secondly, random forests (RF) as a very popular tool in machine learning is also suggested and is presented as a new way to improve the recall phase of CBR in order to further improve it for medical data. Thus, the authors present hybrid systems between case-based reasoning and random forests. The authors show that combining ideas from some classifiers can lead to better performance.