Overview of Machine Learning Part 1
Farhad Maleki, Katie Ovens, Keyhan Najafian, Behzad Forghani, Caroline Reinhold, Reza Forghani
Abstract
The extensive body of research and advances in machine learning (ML) and the availability of a large volume of patient data make ML a powerful tool for producing models with the potential for widespread deployment in clinical settings. This article provides an overview of the classic supervised and unsupervised ML methods as well as fundamental concepts required for understanding how to develop generalizable and high-performing ML applications. It also describes the important steps for developing a ML model and how decisions made in these steps affect model performance and ability to generalize.
Topics & Concepts
MedicineMachine learningArtificial intelligenceSoftware deploymentData scienceComputer scienceMedical physicsSoftware engineeringArtificial Intelligence in HealthcareMachine Learning in HealthcareAI in cancer detection