Enhancing Cancer Treatment Accuracy Through Genetic Mutation Analysis

Cancer remains one of the leading causes of mor- tality worldwide, and accurate prediction of effective treatment strategies plays a vital role in improving patient outcomes. This study focuses on enhancing cancer treatment accuracy through the analysis of genetic mutation data derived from the METABRIC RNA and mutation dataset. Machine learn- ing models such as Random Forest and Logistic Regression were implemented to identify key genetic and clinical features influencing treatment decisions. The model was trained and validated using feature-engineered data, achieving a high accu- racy rate in predicting optimal treatment outcomes. The results demonstrate that integrating genomic information with compu- tational intelligence can significantly assist in selecting precise therapeutic options, reducing the dependency on generalized treatment protocols. This approach highlights the potential of AI-driven analysis in the advancement of personalized cancer therapy and precision medicine.