This is an Open Access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0) (http://creativecommons.org/licenses/by/4.0)
Shrikrishna S Balwante , Dr Mona Dwivedi
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 tumor 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.
1) Abdalla, S. M., Almuhammadi, S., Olayan, A., & Elhoseny, M. (2020). A novel feature selection approach for predicting kidney cancer using decision tree-based classification. Neural Computing and Applications, 32(2), 583-596.
2) Bocian, M., K?pczy?ski, ?., Trela, K., & J?drzejowicz, P. (2018). Correlation-based feature selection for classification of kidney cancer with decision tree and naïve Bayes classifiers. International Journal of Medical Informatics, 116, 94-101.
3) Halilovic, A., Merdanovic, I., & Alibegovic, E. (2019). Early detection of kidney cancer using convolutional neural network. Acta Informatica Medica, 27(4), 283-287.
4) Lu, L., Shi, L., Su, Y., Zhang, Z., & Ling, Y. (2019). A comparative study of kidney cancer patient recognition based on decision tree and naïve Bayes. Journal of Healthcare Engineering, 2019, 1-11.
5) Wu, X., Li, J., Jiang, Y., & Zhang, Y. (2016). A comparative study of traditional machine learning models and deep learning models in identifying kidney cancer. Computers in Biology and Medicine, 79, 231-238.
6) Hsiao, Y-S., et al. (2017). An integrated feature selection and classification approach for cancer subtype prediction. Scientific Reports, 7(1), 1-11.
7) Patel, V., et al. (2016). Kidney cancer survival prediction using decision tree and artificial neural network techniques. Cancer Informatics, 15(1), 53-60.
8) Tizhoosh, H. R., et al. (2019). Identification of Kidney Cancer from CT Scan Images via Pattern Analysis Techniques. Journal of Computing and Information Science in Engineering, 19(3), 1-9.
9) Uguz, H., et al. (2016). Prediction of kidney cancer survival outcomes using decision tree and naïve Bayes classifiers. Journal of Medical Systems, 40(4), 1-6.
10) Zhang, X., et al. (2016). A Novel SVM-Based Method for Automatic Kidney Cancer Identification in CT Scans. Journal of Digital Imaging, 29(3), 324-335.
11) Liu, D., et al. (2019). Deep learning-based feature selection for predicting kidney cancer progression. Journal of American Medical Informatics Association, 26(12), 1487-1494.
Research Scholar, Department of Computer Science and Engineering, Mansarovar Global University (MGU), Bilkisganj, Sehore, Madhya Pradesh, India
No. of Downloads: 22 | No. of Views: 213