Bayes' Theorem - Medical Diagnosis (Using Confusion Matrix)

This example demonstrates the application of Bayes' Theorem in medical diagnosis using a confusion matrix. Bayes' Theorem is an important concept in probability theory that allows us to determine the probability of an event occurring based on conditional probabilities. A confusion matrix is a matrix used to describe the error rate of a classifier. In this example, we use Bayes' Theorem and a confusion matrix to calculate the accuracy of medical diagnosis. The key mathematical concepts involved include Bayes' Theorem, probability theory, and statistics. The construction of the example involves using GeoGebra's Point, OrthogonalLine, Circle, Intersect, Segment, and Textfield commands. This example is suitable for students learning statistics and probability theory.