Data Mining in Cancer Diagnosis and Prediction: Review about Latest Ten Years

Abstract

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.