Multivariate Regression Analysis for Coronary Heart Disease Using SPM Tool

Regression is a data mining (machine learning) technique used to fit an equation to a dataset. In statistical modeling, regression analysis is a statistical process for estimating the relationships among variables. It includes many techniques for modeling and analyzing several variables, when the focus is on the relationship between a dependent variable and one or more independent variables (or 'predictors'). More specifically, regression analysis helps one understand how the typical value of the dependent variable (or 'criterion variable') changes when any one of the independent variables is varied, while the other independent variables are held fixed. Most commonly, regression analysis estimates the conditional expectation of the dependent variable given the independent variables – that is, the average value of the dependent variable when the independent variables are fixed. Then can implement multivariate attributes in coronary heart disease datasets. In regression, analyze the multivariate attribute model which is a generalization of the probit model used to estimate several correlated binary outcomes jointly. In this project, perform comparative study to various regression algorithms such as CART, Ensemble and bagger, Random forest and MARS. Classification and regression trees (CART), Ensemble and bagger, Random forest for tree prediction models can be implemented in prediction model for heart disease datasets. And also propose the MARS procedure which builds flexible regression models by fitting separate splines for multiple predicted variables. Finally compare the results in terms of RMSE, MSE, MAD and gain values metrics in data mining.