Thyroid Cancer

The medical community strives continually to improve the quality of care patients receive.Predictions of prognosis are essential for doctors and patients to choose a course of treatment. Recent yearshave witnessed the development of numerous new cancer survival prediction models. Most attempts topredict the prognosis of people with malignant development rely on classification techniques. We couldexperiment with significantly different results using only a subset of SEER (Surveillance, Epidemiology,and End Results) data. These models were created using machine learning techniques by selecting univariatefeatures and calculating correlations. We illustrated the variation in results and discrepancy of impuritythat can result from varying data quantities and critical factors. Seventeen crucial factors were identifiedto evaluate the effectiveness of an estimation technique. The most effective machine learning algorithms areLogistic Regression, Gradient Boosting Classifier, Random Forest, Extra Trees, Light Gradient Boost, AdaBoost Classifier, and Hist Gradient Boosting