Machine learning in medicine: a practical introduction.

ABSTRACT:

Background

Following visible successes on a wide range of predictive tasks, machine learning techniques are attracting substantial interest from medical researchers and clinicians. We address the need for capacity development in this area by providing a conceptual introduction to machine learning alongside a practical guide to developing and evaluating predictive algorithms using freely-available open source software and public domain data.

Methods

We demonstrate the use of machine learning techniques by developing three predictive models for cancer diagnosis using descriptions of nuclei sampled from breast masses. These algorithms include regularized General Linear Model regression (GLMs), Support Vector Machines (SVMs) with a radial basis function kernel, and single-layer

SUBMITTER: Sidey-Gibbons JAM 

PROVIDER: S-EPMC6425557 | biostudies-literature | 2019 Mar

REPOSITORIES: biostudies-literature