Predicting Cancer Patients’ Survival Using Random Forests

The increasing amount of data available on the web, coupled with the demand for useful information, has sparked increasing interest in gaining knowledge in large information systems, especially biomedical ones. Health institutions operate in an environment that has been generating thousands of health records about patients. Such databases can be the source of a wealth of information. For instance, these databases can be used to study factors that contribute to the incidence of a pathology and thereby determine patient profiles at the earliest stage of the disease. Such information can be extracted with the help of Machine Learning methods, which are capable of dealing with large amounts of data in order to make predictions. These methods offer an opportunity to translate new data into palpable information and, thus, allows earlier diagnosis and precise treatment options. In order to understand the potential of these methods, we use a database that contain records of cancer patients, which is made publicly available by the Oncocentro Foundation of São Paulo. This database contains historical clinical information from cancer patients of the past 20 years. In this paper we present an initial investigation towards the goal of improving prognosis and therefore increasing the chances of survival among cancer patients. The Random Forest Classification Model was employed in our analysis; this model shows to be a suitable predicting tool for ours purpose. Thus, we intend to present means that allows the design of predictive, preventive and personalized treatments, as well as assisting in the decision making process of the disease.