Intelligent classification techniques for effective analysis of multivariate heart datasets

Abstract

newlineTemporal data analysis and representation of temporal knowledge are essential in recent medical diagnosis systems Temporal data mining techniques focus on the analysis of the past and present data for finding temporal patterns regularities and interdependencies in sets of temporal data This temporal data is essential for effective temporal reasoning in medical decision support systems For instance the classification algorithms are useful in the diagnosis of a disease using a multivariate medical data which were acquired from the hospital environment using different technologies This data may be the combination of different types The nature of this data is more complex Hence it is necessary to propose intelligent methods for the effective analysis medical data In this thesis an intelligent temporal mining based technique has been proposed for mining temporal patterns from clinical databases In order to perform effective temporal reasoning a new temporal query language called ETDMSQL has been proposed and implemented It provides special operators for instant comparison, interval comparison time slice operations and temporal mining operations The proposed query language is very useful newlineto retrieve special features for temporal reasoning newline newline