Improving the classifier accuracy with an integrated approach using medical data – a study
by G. Maragatham; S. Rajendran
International Journal of Medical Engineering and Informatics (IJMEI), Vol. 12, No. 4, 2020

Abstract: As information plays a vital role in the current scenario, fetching of information from the voluminous quantity seems to be challenging. Therefore, the data mining community work on this area to find an improved solutions to help the end users. The end users may be an organisation or may be an ordinary user. The authors have used different classification techniques for the study purpose. The article attempts to analyse the accuracy of classifiers with respect to that of medical data. Dataset from the repository is considered for analysing purpose. Initially a pre-processing step is used on the dataset for finding out the missing values. Next, the resulting dataset is applied to the classifiers to study its performance accuracy. In order to improve the classifier accuracy an attribute selection filter of supervised category is selected. For the analysis purpose the Naïve Bayes classifier is used, the comparative study of the classifier is done with the genetic approach, supervised – best first approach and rank approaches. The study shows that, out of all the integrated approaches, the genetic approach is proven to be on the higher end of accuracy.

Online publication date: Tue, 07-Jul-2020

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