Evaluating the effectiveness of regression algorithms in predicting cancer progression

Cancer is a heterogeneous and dangerous disease that affects millions of patients worldwide. Cancer can be challenging to treat as it advances; thus, early detection and the ability to predict progression are necessary for patient management. This problem currently leads to various regression algorithms being applied in the analysis and prediction of cancer progression, but little research is being done on how well these methods work. This project will evaluate how effective regression algorithms are in predicting cancer progression. We use datasets from previous cancer studies and then simply types such as linear regression, logistic regression, or support vector regressor to perform a comparative study. These metrics, such as accuracy, sensitivity, specificity, etc., would be used to evaluate your model. As it progresses, these research findings can help us understand the best regression analysis algorithms when predicting cancer progression. This data can help doctors better predict what is going on with their patients so that the physicians are trained and equipped to treat them best. Furthermore, the results may aid future studies toward developing and improving regression algorithms for cancer prognostication. Finally, this project could aid cancer research and patient care.