Smart Model Elimination Machine Learning With Potential Medical Application

This work in in submission to National High School Journal of Science

Authors

Eric Su Zhang¹, Benjamin Joseph Michael Standefer¹, Stewart Mayer²

1. St. Mark’s School of Texas, Dallas, Texas
2. Department of Science, St. Mark’s School of Texas, Dallas, Texas

Abstract

Automated Machine Learning (AutoML) has emerged as a popular field of research. We present a literature review of existing AutoML papers and conduct a survey on five popular AutoML frameworks. Typically, these frameworks engage in model selection by requiring every model to be run and assessed, a process both time-intensive and computationally expensive. In response, we propose a novel framework, Smart Model Elimination Machine Learning (SMEML), that strategically eliminates models that are unlikely to yield high accuracy. SMEML demonstrates the ability to achieve comparable accuracy to a traditional brute-force approach while significantly reducing the time required. We also believe this innovation is particularly beneficial to machine learning in healthcare, where efficient and accurate disease prediction is crucial.