A Systematic and Structured Review of Intelligent Systems for Diagnosis of Renal Cancer

Abstract

Background and objective: The most prevalent type of kidney cancer is renal cell cancer which typically takes place at advanced or final stages of cancer. Renal cell cancer spreads rapidly and can be fatal if the disease is not spotted in the early stages. Hence, it is crucial to recognize kidney deformities before it is in its final phase. However, in this context, only a few review articles have been published almost 12 years ago. Hence, a systematic review was conducted to determine work done by various researchers on kidney cancer and to identify the research gaps between the studies so far. Methods: To accomplish a structured review, all the relevant papers were categorized based on the authors' names, year of publication, main objective of the research, system input and output variables and finally remarks which gave erudite information apropos the research which has been reported in various publications. After that, the numerous conclusions regarding diagnosing kidney cancer have been scrutinized. Result: The outcome of this study permitted the effective diagnosis of kidney cancer or renal cancer to be done using the adaptive neuro fuzzy method with a 94% accuracy. Although, many data mining techniques were applied by authors, the accuracy of these methods was lower than the adaptive neuro fuzzy method. This method is useful to identify the aspects of diagnosis of renal cancer more rigorously and ameliorate. Conclusion: Overall, an appropriate platform is described by this chapter to detect the research gaps in the field to analyze kidney or renal cancer for further studies or researches.

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