Cancer patients till this day suffer from the inability of science to predict the causes of the disease before it occurs.  One of the cancers that occupy the minds of many women is the cervical cancer because of the delay in its diagnosis as a result of its multiple and unclear causes, so scientists and researchers need to search for the most causative factors. Machine learning approaches have become one of the best and fastest ways to find associations between symptoms and causes of disease. The use of association rule mining (AR) is very effective if diagnostic features are set up. In this work, feature selection (FS) algorithm named ReliefF is used to reach the most correlated factor, then the Apriori algorithm has been updated to reduce the time and space used, and detects features that are closely related to the class attribute to access most factors that cause cervical cancer. The experimental results of the proposed work indicate a number of cervical cancer risk factors that when combined, indicate a woman's likelihood of developing cervical cancer, which is: the number of years of hormonal contraception is greater than or equal to 15, having any type of cancer or HPV or syphilis or HIV, the number of IUD insertion years exceeded 10, First sexual intercourse smaller than 13 and Number of sexual partners greater than 5. The outcomes of this work help both doctors and women to prevent cancer.

Data Mining [DM] has exceptional and prodigious potential for examining and analyzing the vague data of the medical domain. Where these data are used in clinical prognosis and diagnosis. Nevertheless, the unprocessed medical data are widely scattered, diverse in nature, and voluminous. These data should be accumulated in a sorted out structure. DM innovation and creativity give a customer a situated way to deal with new fashioned and hidden patterns in the data. The advantages of using DM in medical approach are unbounded and it has abundant applications, the most important: it leads to better medical treatment with a lower cost. Consequently, DM algorithms have the main usage in cancer detection and treatment through providing a learning  rich environment which can help to improve the quality of clinical decisions. Multi researches are published about the using of DM in different destinations in the medical field. This paper provides an elaborated study about utilization of DM in cancer prediction and classifying, in addition to the  main features and challenges in these researches are introduced in this paper for helping  apprentice and youthful scientists and showing for them the key principle issues that are still exist around there.