Lung Cancer Detection Using Chi-Square Feature Selection and Support Vector Machine Algorithm
Prabhpreet Kaur, N Banerjee, S Das, S Bharati, P Podder, R Mondal, A Mahmood, M Al-Masud, S Bhatia, Y Sinha, L Goel
Abstract
Lung Cancer is the most general type of disease in theworld ofcancer. It affects the lungs of the human body. So, the prediction of lung cancer at its earlier stage is difficult. It is the deadliest cause of death in both men and women. Its symptoms are harder to recognize in the initial stages.Machine learning algorithms have made the prediction and detection of lung cancereasier. Chi-square is used for feature selection to select the relevant features in the lung cancer dataset. Different Machine Learning algorithms are used to predict Lung Cancer.The algorithmsutilized in the proposed work are SVM and Random Forest. We have compared these algorithms with and without feature selection (Chi-square). SVM is identified as the best algorithm in the proposed work due to its accuracy and less execution time for detecting the model. The key objective of this paper is to enhance the accuracy and reduce the execution time of the classifier.