Decision tree
Decision tree
A Decision tree (pronunciation: /dɪˈsɪʒən triː/) is a graphical representation of possible outcomes to a certain decision, based on various conditions. It is widely used in medicine, machine learning, and statistics to visualize complex decision-making processes.
Etymology
The term "decision tree" is derived from the tree-like structure of the diagram, which starts with a single node, then branches off into a number of solutions, just like a tree.
Definition
A decision tree is a flowchart-like structure in which each internal node represents a "test" on an attribute (e.g. whether a patient has a certain symptom), each branch represents the outcome of the test, and each leaf node represents a class label (decision taken after computing all attributes). The paths from root to leaf represent classification rules.
Use in Medicine
In medicine, decision trees can be used to guide the diagnostic process, treatment selection, and predicting patient outcomes. For example, a decision tree might be used to determine the best treatment plan for a patient with cancer based on their age, type of cancer, stage of cancer, and other health factors.
Related Terms
- Classification tree: A type of decision tree that is used for classification problems.
- Regression tree: A decision tree that is used for regression problems.
- Random forest: A machine learning algorithm that uses multiple decision trees to make predictions.
- CART (Classification and Regression Trees): A decision tree learning technique that outputs either classification or regression trees.
External links
- Medical encyclopedia article on Decision tree
- Wikipedia's article - Decision tree
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