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Clustering‐based approach for medical data classification
Author(s) -
Kodabagi Mallikarjun M.,
Tikotikar Ahelam
Publication year - 2018
Publication title -
concurrency and computation: practice and experience
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.309
H-Index - 67
eISSN - 1532-0634
pISSN - 1532-0626
DOI - 10.1002/cpe.5079
Subject(s) - centroid , computer science , data mining , cluster analysis , artificial intelligence , classifier (uml) , machine learning , fuzzy logic
Summary Medical data records are growing enormously in present days due to the growth of various medical technologies and population of the globe. It is a difficult task for a medical expert to process these data records to identify and provide treatment regarding the disease of patient. Hence, it is necessary to automate the processing of such medical data using machine learning‐based Medical Decision Support System. In this proposed work, an Effective Fuzzy Rule Classifier (EFRC)–based decision support system is used for analysis of UCI medical dataset for identification of disease of patients. Initially, the method determines the best centroid value for each of the attribute using training dataset. Furthermore, the centroid value is refined using test samples. The refined centroid values of all the attributes are used by the fuzzy classifier for medical data classification. Experimental results have proven that EFRC performs better classification than existing systems in terms of accuracy, sensitivity, and specificity.