An efficient data mining classification approach for detecting lung cancer disease

Abstract

Background: Automated disease classification using machine learning often relies on features derived from segmenting individual objects, which can be difficult to automate. Proposed model is a classification based an efficient approach in which machine learning concepts are used for the detection of Lung cancer diseases. The algorithm obtained encouraging results but requires considerable computational expertise to execute. Furthermore, some benchmark sets have been shown to compare the proposed work model working. Results: We developed user friendly disease prediction model based on PCA and LDA. To validate the method, the proposed method is applied in MATLAB 2014a to achieve high accuracy performance metric and then comparison has been made with ICA and SURF method. Conclusions: The proposed approach offers improved user-friendliness, as feature extraction is performed in an easily editable. As a direct implication, intermediate results are more easily accessible.

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