Cancer has been one of the biggest issues today, The early diagnosis of cancer remains complicated for doctors. When developing novel methods of cancer detection and prevention, It is particularly important to identify genetic and environmental factors. This paper presents the novel approach based on the selection of hybrid features that reduces the dimensionality of features significantly. This paper suggests an efficient Relief and PCA approach that is used on the dataset of cervical cancer. Further, the obtained score is taken as the input for the classification, mechanism. The 3 different classification techniques have been applied. The experiment is conducted on MATLAB. Moreover, The threshold value is experimentally shown to significantly affect the selection of appropriate features. On the basis of many accuracy parameters including accuracy or recall, the experimental result is compared.

Hybrid Feature Selection, Chronic Disease Datasets, PCA, classification techniques, Disease Diagnosis, Relief

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