Augmentation of predictive competence of non-small cell lung cancer datasets through feature pre-processing techniques Online publication date: Tue, 04-Apr-2023
by M. Sumalatha; Latha Parthiban
International Journal of Engineering Systems Modelling and Simulation (IJESMS), Vol. 14, No. 2, 2023
Abstract: Non-small cell lung cancer (NSCLC) comprised of complex hidden and unknown data that is challenging for prediction at the earlier stage. The major objective of the research paper is to develop a novel preprocessing model based on minimisation of features and competency maximisation through feature pre-processing (FPP) to provide augmentation in predictive competence of NSCLC datasets. In Phase-I, the test for relevancy identified behavioural errors like null, empty and NAN values to reduce two features. In Phase-II, regression analysis was performed to find the relationship between features after which four features were removed. In Phase-III, cluster analysis is carried out to find the irrelevant features in the form of clusters and seven features are removed. The competency of NSCLC dataset before FPP showed more accuracy than after FPP with classifiers like simple tree, complex tree, linear SVM, Gaussian SVM, weighted KNN and boosted tree classifiers.
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