Systematic analysis of applied data mining based optimization algorithms in clinical attribute extraction and classification for diagnosis of cardiac patients

This chapter covers the data mining techniques applied to the processing of clinical data to detect cardiovascular diseases. Technology evaluation and rapid development in medical diagnosis have always attracted the researchers to deliver novelty. Chronic diseases such as cancer and cardiac have been under discussion to ease their treatments using computer aided diagnosis (CAD) by optimizing their architectural complexities with better accuracy rate. To design a medical diagnostic system, raw ECG Signals, clinical and laboratory results are utilized to proceed further processing and classification. The significance of an optimized system is to give timely detection with lesser but essential clinical attributes for a patient to ensue surgical or medical follow-up. Such appropriate diagnostic systems which can detect abnormalities in clinical data and signals are truly vital and various soft computing techniques based on data mining have been applied. Hybrid approaches derived from data mining algorithms are immensely incorporated for extraction and classification of clinical records to eliminate possible redundancy and missing details which can cause worse overhead issues for the designed systems. It also extends.