• Rishi Reddy Kothinti

Oncology professionals now use personalized cancer treatment as a critical procedure that develops unique treatments for individual patient data. Annotation algorithms in machine learning have transformed this domain because they boost diagnosis precision and treatment planning accuracy. This paper examines the impact of ML on cancer treatment plans which involves biological analysis through genomic assessment predictive modeling and treatment optimization methods. Deep learning and support vector machines join reinforcement learning as essential ML methods which lead to better clinical results because they help detect diseases early, identify risk levels, and adapt treatments instantly. The accomplishment of ML in treatment personalization faces lingering obstacles, such as data protection issues and difficulties in making models understandable by human users and practice adoption. The research explores contemporary ML applications in personalized oncology through an extensive review and discussions about potential clinical impacts that cope with technical and moral challenges.

Machine Learning, Personalized Cancer Treatment, Predictive Modeling, Genomic Analysis, Oncology

"THE IMPACT OF MACHINE LEARNING ALGORITHMS IN PERSONALIZED CANCER TREATMENT PLANS.", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 9, page no.g318-g328, September-2024, Available :http://www.jetir.org/papers/JETIR2409634.pdf

"THE IMPACT OF MACHINE LEARNING ALGORITHMS IN PERSONALIZED CANCER TREATMENT PLANS.", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 9, page no. ppg318-g328, September-2024, Available at : http://www.jetir.org/papers/JETIR2409634.pdf