Evaluation prediction techniques to achieve optimal biomedical analysis

Abstract

Intelligent analysis of prediction data mining techniques is widely used to support optimising future decision-making in many different fields including healthcare and medical diagnoses. These techniques include Chi-squared Automatic Interaction Detection (CHAID), Exchange Chi-squared Automatic Interaction Detection (ECHAID), Random Forest Regression and Classification (RFRC), Multivariate Adaptive Regression Splines (MARS), and Boosted Tree Classifiers and Regression (BTCR). This paper presents the general properties, summary, advantages, and disadvantages of each one. Most importantly, the analysis depends upon the parameters that have been used for building a prediction model for each one. Besides, classifying those techniques according to their main and secondary parameters is another task. Furthermore, the presence and absence of parameters are also compared in order to identify the better sharing of those parameters among the techniques. As a result, the techniques with no randomness and mathematical basis are the most powerful and fast compared with the others.