Soft Computing based Approaches for classification of medical data

Abstract

In recent years, the development of clinical decision support system (CDSS) has gained newlinesignificant attention in research. These decision support systems play a substantial role in newlinemedical domain by helping physicians for making clinical decisions. The clinicians can newlineinteract with the clinical decision support system for the diagnosis and analysis of the newlinepatient s data. The researchers are trying several data mining methods for analyzing and newlineinterpreting large amount of medical data to help experts for correct diagnosis. newlineHowever, the growing size and characteristics of medical data poses several challenges for newlineanalysis and interpretation. The medical data is characterized by missing values, high newlinedimensionality, presence of noise, complex relations and unnecessary features. Hence, it is newlinenecessary to develop technological solutions to pre-process the medical data, extract newlineimportant features, build association rules and perform classification. Therefore, the newlinedevelopment of techniques for medical data classification has gained significant interest. newlineThe automation of medical data classification assists physician or experts to take right newlinedecision to improve the quality of patient s care. In this research, it is proposed to develop newlinecomputational methods for medical data classification using soft computing. The main newlinereason for choosing soft computing is to exploit its capabilities like: human mind analyzing, newlinereasoning, thinking, tolerating partial truth, precision, uncertainty, learning from past newlinerecords and extracting useful information to achieve an efficient and low-cost solution newline