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Background: Lung cancer was known as primary cancers and the survival rate ofcancer is about 15%. Early detection of lung cancer is the leading factor insurvival rate. All symptoms (features) of lung cancer do not appear until thecancer spreads to other areas. It needs an accurate early detection of lungcancer, for increasing the survival rate. For accurate detection, it needcharacterizes efficient features and delete redundancy features among allfeatures. Feature selection is the problem of selecting informative featuresamong all features. Materials and Methods: Lung cancer database consist of 32patient records with 57 features. This database collected by Hong and Youngandindexed in the University of California Irvine repository. Experimentalcontents include the extracted from the clinical data and X-ray data, etc. Thedata described 3 types of pathological lung cancers and all features are takingan integer value 0-3. In our study, new method is proposed for identifyefficient features of lung cancer. It is based on Hyper-Heuristic. Results: Weobtained an accuracy of 80.63% using reduced 11 feature set. The proposedmethod compare to the accuracy of 5 machine learning feature selections. Theaccuracy of these 5 methods are 60.94, 57.81, 68.75, 60.94 and 68.75.Conclusions: The proposed method has better performance with the highest levelof accuracy. Therefore, the proposed model is recommended for identifying anefficient symptom of Disease. These finding are very important in healthresearch, particularly in allocation of medical resources for patients whopredicted as high-risks

Hyper-Heuristic Algorithm for Finding Efficient Features in Diagnose of Lung Cancer Disease