Maximum Accuracy Machine Learning Statistical Analysis-A Novel Approach

Shannon Ugarte; Paul Yarnold; Paul Ray; Kevin Knopf; Shamia Hoque; Matthew Taylor; Charles L Bennett

doi:10.1007/978-3-031-04402-1_8

Maximum Accuracy Machine Learning Statistical Analysis-A Novel Approach

Cancer Treat Res. 2022:184:113-127. doi: 10.1007/978-3-031-04402-1_8.

Authors

Shannon Ugarte¹, Paul Yarnold¹, Paul Ray¹, Kevin Knopf¹, Shamia Hoque¹, Matthew Taylor¹, Charles L Bennett²

Affiliations

¹ SONAR (Southern Network on Adverse Reactions) Program, University of South Carolina College of Pharmacy, Columbia, SC, 29208, USA.
² SONAR (Southern Network on Adverse Reactions) Program, University of South Carolina College of Pharmacy, Columbia, SC, 29208, USA. bennettc@cop.sc.edu.

PMID: 36449192
DOI: 10.1007/978-3-031-04402-1_8

Abstract

Logistic regression is a statistical tool of paramount significance in the field of epidemiology¹ and ranks as one of the most frequently published multivariable analyses for designs involving a single binary dependent variable and one or more independent variables in the fields of public health^2,3 and medical⁴ research.

MeSH terms

Humans
Machine Learning*