• Ms.M.Anusha
• M.Jayasri
• Ch.Navya Jeevani
• M.Afreen Khan
• N.Karuna Sree

Cancer is a heterogeneous disease that has many subtypes. Early diagnosis and prognosis are imperative in cancer such that it might increase the odds of long-term survival. The need to group patients with different cancer types on a risk scale has prompted numerous scientific teams exploring both biomedical and bioinformatics backgrounds try to apply Machine Learning (ML) methods to the problem, where one of the attractive benefits of genomic data is patient clustering. Consequently, these methods are abstracted in an attempt to mimic the development and treatment of cancerous diseases. Further, it demonstrates how ML tools are capable of identifying from rather messy datasets what must be salient features. Many such techniques, e.g., Artificial Neural Networks (ANNs), Bayesian Networks (BNs), Support Vector Machines (SVMs).

Bayesian Networks, Artificial Neural Networks, Support Vector Machines, Machine Learning, Decision Tree, Epidemiology, Categorical Encoding, Repository

"Cancer Prognosis using Machine Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 11, page no.356-363, November-2024, Available :http://www.jetir.org/papers/JETIRGO06036.pdf

"Cancer Prognosis using Machine Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 11, page no. pp356-363, November-2024, Available at : http://www.jetir.org/papers/JETIRGO06036.pdf