Online ISSN: 2515-8260

An Empirical Study of Machine Learning Algorithms forCancer Identification

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A major challenge in the search for a cure for cancer is predicting the illness status of the disease. For instance, distinguishing between benign and malignant tumours helps doctors diagnose cancer more accurately. Although technology advancements produced data on patients with various illness stages, it would be crucial to assess how well machine learning algorithms accomplish predictions. In this article, we suggest employing machine learning algorithms such a variation of AdaBoost, deepboost, xgboost, and support vector machines. We then analyse them using area under curve and accuracyon actual clinical data linked to thyroid cancer, colon cancer, and liver cancer. Results from experiments demonstrate the SVM's strong performance.

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