Effects of data transformation methods on classification of patients diagnosed with myocardial infarction

Large datasets may contain redundant data. Variable selection methods that select most relevant variables in the data set, fail to consider the interaction between the variables. Data transformation methods are used to transfer the original data to a new dimension and capture the most significant information within the data set. The data set used in this study was based on 45 clinical variables collected from 697 patients diagnosed as either having myocardial infarction (MI) or not. Principal component analysis (PCA) and independent component analysis (ICA) were applied prior to classification of patients to MI or Non-MI groups using support vector machines (SVM).