What Types of Classification Algorithms are Used in Cancer Research?
Several classification algorithms are employed in cancer research, each with its strengths and limitations. Some of the most common ones include:
Decision Trees: These algorithms create a model that predicts the value of a target variable by learning simple decision rules inferred from the data features. Support Vector Machines (SVM): SVMs are effective in high-dimensional spaces and are used for binary classification tasks, often applied in cancer subtype classification. Random Forests: An ensemble method that builds multiple decision trees and merges them to get a more accurate and stable prediction. Neural Networks: Particularly deep learning models, have shown great promise in tasks involving large and complex datasets such as those in cancer genomics and imaging. k-Nearest Neighbors (k-NN): A simpler algorithm that classifies data based on the majority class among its neighbors, used in some cancer prediction tasks.
