Advancing Cancer Treatment Protocols through the Application of Machine Learning and Operational Research

Abstract

In order to optimise cancer treatment regimens, this research article investigates the merging of Machine Learning (ML) and Operational Research (OR) methodologies. Conventional cancer treatments like radiation and chemotherapy often have major side effects and inconsistent patient results because of their imprecise nature. Predictive models may foretell treatment responses by analysing big datasets with the use of machine learning (ML). This enables more customised and adaptable cancer therapy. In addition, OR methods like as stochastic modelling and linear programming may optimise medication doses, treatment plans, and resource distribution, enhancing the general efficacy and efficiency of healthcare service. The study looks at how OR's optimisation frameworks and ML's predictive powers may work together to create customised, data-driven treatment regimens that improve patient outcomes, reduce side effects, and make the most use of available resources. The results indicate that there is great potential to advance cancer treatment and enhance patient survival and quality of life by incorporating these strategies into clinical practice.