Sunil Kr. Jha1,*, Zulfiqar Ahmad2
CMES-Computer Modeling in Engineering & Sciences, Vol.129, No.2, pp. 567-594, 2021, DOI:10.32604/cmes.2021.016817
Abstract Health care data mining is noteworthy in disease diagnosis and recognition procedures. There exist several
potentials to further improve the performance of machine learning based-classification methods in healthcare data
analysis. The selection of a substantial subset of features is one of the feasible approaches to achieve improved
recognition results of classification methods in disease diagnosis prediction. In the present study, a novel combined
approach of feature generation using latent semantic analysis (LSA) and selection using ranker search (RAS) has
been proposed to improve the performance of classification methods in lymph disease diagnosis prediction. The
performance of the proposed combined approach… More >
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