An Identified Kidney Cancer Using Decision Tree and Naïve  Bayes Algorithm in Data Mining

Shrikrishna S Balwante; Mona Dwivedi

doi:10.55524/ijircst.2023.11.5.5

Authors

Shrikrishna S Balwante Research Scholar, Department of Computer Science and Engineering, Mansarovar Global University (MGU), Bilkisganj, Sehore, Madhya Pradesh, India Author
Mona Dwivedi Professor, Department of Computer Science and Engineering, Mansarovar Global University(MGU), Bilkisganj, Sehore, Madhya Pradesh, India Author

DOI:

https://doi.org/10.55524/ijircst.2023.11.5.5

Keywords:

Data Mining, Decision Tree, CKD, Naïve BayeS Algorithms

Abstract

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.

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References

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