An Identified Kidney Cancer Using Decision Tree and Naïve Bayes Algorithm in Data Mining (International Journal of Innovative Research in Computer Science & Technology, 2023)
Several clients with kidney cancer are  able to receive curative treatment because there is  nowadays no way to detect the cancer in its initial stages.  To decrease the likelihood of kidney tumour cells and the  need for transplant, it is important to be able to predict  kidney cancer at an early stage so that service users can  begin appropriate therapy and treatment. Thanks to  advancements in AI, automated cancer diagnostic tools  have been developed. These degree of excellence many  unique deep learning and machine learning algorithms.  Extracting intelligent and predictive models from large  datasets is possible through the use of data mining. Data  mining is the practise of gaining insight from massive  datasets. It fuses time-honoured techniques to analyse  data with cutting-edge mathematical advances to handle  massive datasets. Concepts from several other fields are  incorporated into it as well, making it a multidisciplinary  field. These fields include database frameworks,  measurements, AI, the figuring data hypothesis, and  example recognition. Using a combination of the decision  tree algorithm and the naive Bayes data mining technique,  the proposed model was able to successfully identify  cases of kidney cancer in this study.
Keywords: Data Mining; Decision Tree; CKD; Naïve BayeS Algorithms