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A Method to Detection of Prostate Cancer and Treatments
Abstract
Data mining refers to the extracting or mining knowledge from large amounts of data. Classification according to kinds of database mind. Classification is a two step process only, using all fields. In spite of increased prostate cancer patients, little is known about impact of treatments for prostate cancer begins when healthy cells in the prostate change and grow out of control forming a tumor. Here our proposed method works on finding the correct stages of prostate cancer so the best treatment can be given to the patients accordingly. Here, the existing system c4.5 algorithm has been simply applied on synthesized prostate cancer datasets. However, main drawback of this existing algorithm is that the discovery of interesting or useful rules. More over the number of rules less. So, here try to develop a new method by capturing the important attributes influence to get more accurate result. Here integrate the k-means algorithm and apriori algorithm with the c4.5 algorithm. Due to dealing with the large amount of database, a variety of decision tree classification algorithm has been considered. The advantages of c4.5 decision tree algorithm is significantly, so it can be choose.
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References
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DOI: http://dx.doi.org/10.36039/AA032018001.
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