From Data to Diagnosis: Predicting Cancer Types with Machine Learning

Oncology is a diagnostic field focused on determining whether a tumor is malignant and facilitating appropriate cancer treatment. Machine learning has significantly transformed medical diagnostics, particularly within oncology. Therefore, our objective is to employ machine learning techniques to analyze different cancer types using a variety of genomic, proteomic, and clinical data. This paper designs an improved diagnostic framework utilizing various machine learning algorithms for analyzing cancer and enable tailored treatment strategies. The experimental findings demonstrate that our method can effectively distinguish between different cancer types with an accuracy of approximately 99%. Also, this paper highlights the potential of integrating computational techniques into clinical settings, setting the stage for more accurate and timely cancer diagnoses, ultimately leading to better patient outcomes.