Abstract
Due to increasing cancer cases around the world, Lung cancer has become the favorite topic of research for a long period of time. The actual reason is due to the increasing rate of new cases across the globe. Therefore, many researchers used prediction or classification algorithm to identify the factors that contribute to the increase of this deadly disease. Two models were built namely WRF and RF. RF model provides the result of features selected by a predominant feature selection method whereas WRF model provides result of all features without performing any selection process. A comparison is made to inform the importance of selecting the feature for classification or prediction algorithm. The accuracy provided by WRF model is higher than RF model which highlights the importance of selecting the feature for classification algorithm.
Highlights
Around the globe, Lung cancer (LC) is most repeatedly identified cancer in 37 countries and it is responsible for high death rate in males [1]
Dataset with all features are loaded to WRF and dataset with features selected by Pearson correlation (PC) are loaded into RF
Dataset with all features are loaded to WRF and dataset with features selected by PC are loaded intoRF
Summary
Lung cancer (LC) is most repeatedly identified cancer in 37 countries and it is responsible for high death rate in males [1]. Lung cancer patients have higher survival rate, once detected earlier. There are many histological categorizations in the Lung cancer cells [2]. Based upon the size of the cancer cell, they are classified into many types [3]. Certain type ofcancer cells is frequently found in heavy smokers than non-smokers, the progress of particular type of lung cancer cell is higher in non-smokers [4]. Though there are many parameters contributing to the development of Lung cancer, the exact reason is not known. Many prediction and classification algorithms are used to find out features that contribute to this deadlydisease.
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