Lung Cancer Prediction Using Stochastic Diffusion Search (SDS) Based Feature Selection and Machine Learning Methods

TLDR: The results of the experiment prove that the proposed algorithm of feature selection that is wrapper-based is capable of achieving better levels of performance compared to existing methods like minimum redundancy maximum relevance, and correlation-based feature selection.

Abstract: The symptoms of cancer normally appear only in the advanced stages, so it is very hard to detect resulting in a high mortality rate among the other types of cancers. Thus, there is a need for early prediction of lung cancer for the purpose of diagnosing and this can result in better chances of it being able to be treated successfully. Histopathology images of lung scan can be used for classification of lung cancer using image processing methods. The features from lung images are extracted and employed in the system for prediction. Grey level co-occurrence matrix along with the methods of Gabor filter feature extraction are employed in this investigation. Another important step in enhancing the classification is feature selection that tends to provide significant features that helps differentiating between various classes in an accurate and efficient manner. Thus, optimal feature subsets can significantly improve the performance of the classifiers. In this work, a novel algorithm of feature selection that is wrapper-based is proposed by employing the modified stochastic diffusion search (SDS) algorithm. The SDS, will benefit from the direct communication of agents in order to identify optimal feature subsets. The neural network, Naive Bayes and the decision tree have been used for classification. The results of the experiment prove that the proposed method is capable of achieving better levels of performance compared to existing methods like minimum redundancy maximum relevance, and correlation-based feature selection.