Abstract:
The research in the medical domain is clinical in its nature but with the advancement of
information technology the trends of researchers in health care sector has been moving towards
medical informatics. Usage of data mining techniques plays a major role in medical informatics.
Especially when it comes to predicting or forecasting the survivability of a disease which is known as
medical prognosis, data mining plays a major role. With the time medical prognosis is becoming highly
important to increase the morality of patients especially who are diagnosed as cancer victims. Although
the importance is increasing in the cancer prognosis, the methods that are in the practice for predicting
still need to be improved and refined. In this paper we present an overview of the current research
being carried out using the data mining techniques for prognosis of cancers. The goal of this study is to
identify the well-performing data mining algorithms used on medical databases in order to predict
survivability of cancer patients. The following algorithms have been identified: Decision Trees,
Support Vector Machine, Artificial Neural Networks, Naïve Bayes and Fuzzy Rules. Analyses show
that it is very difficult to name a single data mining algorithm as the most suitable for cancer prognosis.
At times some algorithms perform better than others, but there are cases when a combination of the
best properties of some of the aforementioned algorithms together results more effective.
