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The diagnosis of medical issues often requires knowledge of a wide range of symptoms and their effects. Evidence of this fact is the number of highly specialized disciplines within the medical field. In many cases, there is simply too much information for any one person to digest and apply in real scenarios. By using machines, especially machines which can learn over time, medical professionals will be better equipped to diagnose a wider range of problems—with greater accuracy.
Diagnosis v1.0 is a medical diagnostic tool that can assist with patient diagnosis. The application learns to diagnose patients much the same way a human doctor does: by observation. Various observations, along with their diagnosis, are supplied to the agent. The agent is able to use this information as a basis for future diagnoses.
The primary learning mechanism employed by Diagnosis v1.0 is a structure known as a decision tree. Decision trees are highly efficient at classifying informa
