Hyper-Heuristic Algorithm for Finding Efficient Features in Diagnose of
  Lung Cancer Disease
  Background: Lung cancer was known as primary cancers and the survival rate of
cancer is about 15%. Early detection of lung cancer is the leading factor in
survival rate. All symptoms (features) of lung cancer do not appear until the
cancer spreads to other areas. It needs an accurate early detection of lung
cancer, for increasing the survival rate. For accurate detection, it need
characterizes efficient features and delete redundancy features among all
features. Feature selection is the problem of selecting informative features
among all features. Materials and Methods: Lung cancer database consist of 32
patient records with 57 features. This database collected by Hong and Youngand
indexed in the University of California Irvine repository. Experimental
contents include the extracted from the clinical data and X-ray data, etc. The
data described 3 types of pathological lung cancers and all features are taking
an integer value 0-3. In our study, new method is proposed for identify
efficient features of lung cancer. It is based on Hyper-Heuristic. Results: We
obtained an accuracy of 80.63% using reduced 11 feature set. The proposed
method compare to the accuracy of 5 machine learning feature selections. The
accuracy 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 level
of accuracy. Therefore, the proposed model is recommended for identifying an
efficient symptom of Disease. These finding are very important in health
research, particularly in allocation of medical resources for patients who
predicted as high-risks
