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A Deep Learning Strategy for Predicting Liver Cancer Using Convolutional Neural Network Algorithm
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One of the common types of cancer is liver cancer, early detection and diagnosis of which are critical. Discovery, decision, and aggressive therapy can prevent most cancer deaths. We use data mining approaches (Convolutional Neural Networks) to build prediction models for liver cancer with the most widely used statistical analysis methodology. Around 579 records and 10 variables were included in the data collection. The model was built, evaluated, and compared using a k-fold cross-validation process. CNN was the best accurate predictor for this domain with a test set accuracy of 100%.
Keywords
Cancer, Convolutional Neural Networks, data mining, statistical analysis.
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- M. B. Priya, P. L. Juliet, and P. R. Tamilselvi, "Performance analysis of liver disease prediction using Machine Learning Algorithms," Int. Res. J. Eng. Technol., vol. 5, no. 1, pp. 206-211, 2018, [Online]. Available: https://www.irjet.net/archives/V5/i1/IRJET-V5I142.pdf
- T. R. Baitharu, and S. K. Pani, "Analysis of data mining techniques for healthcare decision support system using liver disorder dataset," Procedia Comput. Sci., vol. 85, pp. 862-870, 2016, doi: 10.1016/j.procs.2016.05.276.
- B. M. Ramageri, "Data mining techniques and applications," Indian J. Comput. Sci. Eng. vol. 1, no. 4, pp. 301-305. [Online]. Available: http://www.ijcse.com/docs/IJCSE10-01-04-51.pdf
- D. Anwanwan, S. K. Singh, S. Singh, V. Saikam, and R. Singh, "Challenges in liver cancer and possible treatment approaches," Biochim. Biophys Acta Rev. Cancer, vol. 1873, no. 1, p. 188314, 2020, doi: 10.1016/j.bbcan.2019.188314.
- A. Kumar, S. K. Acharya, S. P. Singh, A. Arora, R. K. Dhiman, R. Aggarwal, and A. C. Anand et al. "2019 Update of Indian National Association for Study of the Liver Consensus on Prevention, Diagnosis, and Management of Hepatocellular Carcinoma in India: The Puri II Recommendations," J. Clin. Exp. Hepatol., vol. 10, no. 1, pp. 43-80, 2020, doi: 10.1016/j.jceh.2019.09.007.
- K. Nguyen,C. Fokes, A. Ross, and S. Sridharan., "Iris recognition with off-the-shelf CNN features: A deep learning practice," IEEE Access, vol. 6, pp. 18848-18855, 2018, doi: 10.1109/ACCESS.2017.2784352.
- L. Alzubaidi, J. Zhang, A. J. Humaidi, A. Al-Dujaili, Y. Duan, O. Al-Shamma, J. Santamaria, M. A. Fadhel, M. Al-Amidie, and L. Farhan, "Review of deep learning?: Concepts , CNN architectures, challenges , applications, future directions.," J. Big Data, vol. 8, 2021, Art. No. 53, doi:10.1186%2Fs40537-021-00444-8.
- A. N. Arbain and B. Y. P. Balakrishnan, "A comparison of data mining algorithms for liver disease prediction on imbalanced data,"Int. J. Data Sci. Adv. Analytics, vol. 1, no. 1, 2019. [Online]. Available: http://ijdsaa.com/index.php/welcome/article/view/2
- E. Mamdouh and M. Mabrouk, "A study of support vector machine algorithm for liver disease diagnosis," 2014.[Online]. Available: https://www.researchgate.net/publication/272356715
- S. R. Ghosh and S. Waheed, "Analysis of classification algorithms for liver disease diagnosis," J. Sci. Technol. Environ. Inf., vol. 5, no. 1, pp. 361-370, 2017, doi:10.18801, jstei.050117.38.
- P. Rajewari and G. S. Reena, "Analysis of liver disorder using data mining algorithm," Global J. Comput. Sci. Technol.,vol. 10, no. 14, pp. 48-52, 2010.
- P. Ramachandran, N. Girija, and T. Bhuvaneswari, "Early detection and prevention of cancer using data mining techniques," Int. J. Comput. Appl., vol. 97, no. 13, pp. 48-53, 2014, doi: 10.5120/17069-7492.
- Aneeshkumar, A. S., S. Ramachandran, S. Arunachalam, and M. Gupta, "Estimation of liver disorder and its correlation factors in modern world using data mining techniques," Int. J. Advanced Sci. Technol. vol. 29, no. 5, 2020. [Online]. Available: http://sersc.org/journals/index.php/IJAST/article/view/18397
- I. Arshad, C. Dutta, T. Choudhury, and A. Thakral, "Liver disease detection due to excessive alcoholism using data mining techniques," in 2018 Int. Conf. Advances Comput. Communication Eng., 2018, pp. 163-168, doi: 10.1109/ICACCE.2018.8441721.
- N. Nahar and F. Ara, "Liver disease prediction by using different decision tree techniques," Int. J. Data Mining & Knowl. Manage.Process, vol. 8, no. 2, 2018, doi: 10.5121/ijdkp.2018.8201.
- P. Kumar and R. S. Thakur, "Liver disorder detection using variable- neighbor weighted fuzzy K nearest neighbor approach," Multimedia Tools Appl, vol. 80, pp. 16515-16535, 2021, doi: 10.1007/s11042-019-07978-3
- C. A. O. Guogang, L. Mengxue, C. A. O. Cong, W. Ziyi, F. Meng, and G. Chunfang, "Primary liver cancer early screening based on Gradient Boosting Decision Tree and Support Vector Machine," Int. Conf. Intell. Inf. Biomed. Sciences 2019, pp. 287-290, doi: 10.1109/ICIIBMS46890.2019.8991441
- V. J. Gogi and Vijayalaxmi, M.N., "Prognosis of liver disease: Using Machine Learning algorithms," 2018 Int. Conf. Recent Innovations Elect., Electronics & Communication Eng., 2018, pp. 875-879, doi: 10.1109/ICRIEECE44171.2018.9008482.
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