Augmentation of predictive competence of non-small cell lung cancer datasets through feature pre-processing techniques. (3rd February 2023)

• Record Type: Journal Article
• Authors: Sumalatha, M.; Parthiban, Latha
• Is Part Of: International journal of engineering systems modelling and simulation. Volume 14:Number 2(2023)
• Page Start: 86
• Page End: 100
• Publication Date: 2023-02-03
• Subjects: non-small cell lung cancer -- NSCLC -- competency of prediction -- relevancy analysis -- regression analysis -- cluster analysis -- feature pre-processing model -- feature pre-processing -- FPP -- competency analytics; Engineering systems -- Computer simulation -- Periodicals; Engineering systems -- Mathematical models -- Periodicals; 620.0042
• Languages: English
• ISSNs: 1755-9758

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.