Exploring the Application of Machine Learning to Cancer Prediction
DOI:
https://doi.org/10.61173/81mmj896Keywords:
Machine learning, Prediction, CancerAbstract
This paper explores the wide range of applications of machine learning techniques in the field of cancer, with a particular focus on their specific use in the diagnosis and classification of important cancer types such as lung, oral and breast cancer. The paper concludes that machine learning algorithms can assist physicians in detecting cancerous lesions earlier and improve the accuracy of diagnosis. In addition, the paper explores the importance of machine learning in the early detection and treatment of cancer and its potential for collaboration with clinicians. In the future, collaborations across datasets and across healthcare institutions will drive further development of machine learning algorithms, providing more possibilities for personalized medical diagnosis and treatment plans to maximize patient survival and quality of life. The research in this paper can give relevant readers with insight into the potential and application of machine learning in the field of cancer, as well as its important role in improving the efficiency and quality of healthcare services.
References
average accuracy of 93% in its ability to detect cancer at [1]WorldHealthOrganization.health-topics/cancer.[DB/ an early stage, and the DNLC method outperforms other OL].(2024/4/21).https://www.who.int/zh/health-topics/ methods in all cases. But machine learning is ultimately cancer#tab=tab_1 a machine subject to error and there is no way to achieve [2]WorldHealthOrganization.news-room/fact-sheets/detail/ 100% accuracy, but as a medical classification tool it can cancer.[EB/OL].(2024/4/21).https://www.who.int/zh/newsbe very effective in predictive classification and assessing room/fact-sheets/detail/cancer risk. For example, using machine learning for ct image [3]Nwanosike, E.M., Conway, B.R., Merchant, H.A., & Hasan, classification, to learn the degree of risk of their own can- S.S. (2021). Potential applications and performance of machine cer, only medium and high risk of manual confirmation learning techniques and algorithms in clinical practice: A by the doctor, and low and medium risk for conservative systematic review. International journal of medical informatics, treatment and observation. This can greatly save the labor 159, 104679 . cost of social medical resources and the time people spend [4]IBM.Meachine-learning[DB/OL](2024/4/21),https://www. in the hospital. In today’s society, going to the doctor is ibm.com/topics/machine-learning a very time-consuming affair. Machine learning cannot [5]Wen, X., Guo, X., Wang, S., Lu, Z., & Zhang, Y. (2024). replace traditional healthcare. However it can be used as Breast cancer diagnosis: A systematic review. Biocybernetics a medical tool to improve the efficiency and accuracy of and Biomedical Engineering. medical treatment. [6]He, J.Y., Baxter, S.L., Xu, J., Xu, J., Zhou, X., & Zhang, K. A new angle on cancer research is provided by machine (2019). The practical implementation of artificial intelligence learning, which creates opportunities for the creation technologies in medicine. Nature Medicine, 25, 30 - 36. of decision support systems that will enhance precision [7]Lopez-Perez, L., Georga, E., Conti, C., Vicente, V., García, R., oncology. Early detection and monitoring have become Pecchia, L., Fotiadis, D., Licitra, L., Cabrera, M.F., Arredondo, essential elements in the management of cancer due to M.T., & Fico, G. (2024). Statistical and machine learning
methods for cancer research and clinical practice: A systematic 866. review. Biomedical Signal Processing and Control. [11]Hegde, S., Ajila, V., Zhu, W., & Zeng, C. (2022). Artificial [8]Supriya, M., & Deepa, A.J. (2020). Machine learning intelligence in early diagnosis and prevention of oral cancer. approach on healthcare big data: a review. Big Data and Asia-Pacific journal of oncology nursing, 9(12), 100133. Information Analytics. [12]Balkenende, L., Teuwen, J., & Mann, R. M. (2022). [9]Delzell, D. A. P., Magnuson, S., Peter, T., Smith, M., & Application of Deep Learning in Breast Cancer Imaging. Smith, B. J. (2019). Machine Learning and Feature Selection Seminars in nuclear medicine, 52(5), 584–596. Methods for Disease Classification With Application to Lung [13]Haitham Elwahsh, Medhat A. Tawfeek, A.A. Abd El- Cancer Screening Image Data. Frontiers in oncology, 9, 1393. Aziz, Mahmood A. Mahmood, Maazen Alsabaan, Engy El- [10]Li, Y., Wu, X., Yang, P., Jiang, G., & Luo, Y. (2022). shafeiy,A new approach for cancer prediction based on deep Machine Learning for Lung Cancer Diagnosis, Treatment, and neural learning,Journal of King Saud University - Computer and Prognosis. Genomics, Proteomics & Bioinformatics, 20, 850 - Information Sciences,Volume 35, Issue 6,2023,101565.
Downloads
Published
Issue
Section
License
Copyright (c) 2024 by the authors.
This work is licensed under a Creative Commons Attribution 4.0 International License.
