Abstract
Background: Cancer treatment is expensive and results in a lot of side effects, and thus survival prediction is necessary for the patients as well as the clinician. Data mining technology has been used in the medical domain to extract interesting information. Cancer prognosis is such an application in medicine.
Objective: This study focuses on identifying the technologies used in the recent past for predicting the survival of cancer patients. Supervised, semi-supervised and unsupervised techniques have been used over the years successfully for the survival prediction of different types of cancer.
Methods: A systematic literature review process has been followed in this study to discover the future directions of the research. This study focuses on uncovering the gaps in recent studies.
Results and Conclusion: It has been found that the present system lacks structured information of the patients. Also, there are a lot of different cancer types that are still unexplored in terms of survival prediction, mainly due to the unavailability of sufficient data. Hence a lot can be improved if researchers may get their hands on required data for the research.
Keywords: Cancer, cancer prognosis, data mining, medical, machine learning, preprocessing, survival prediction.
Graphical Abstract
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