Developing an ensemble machine learning study: Insights from a multi-center proof-of-concept study.

ABSTRACT:

Background

To address the numerous unmeet clinical needs, in recent years several Machine Learning models applied to medical images and clinical data have been introduced and developed. Even when they achieve encouraging results, they lack evolutionary progression, thus perpetuating their status as autonomous entities. We postulated that different algorithms which have been proposed in the literature to address the same diagnostic task, can be aggregated to enhance classification performance. We suggested a proof of concept to define an ensemble approach useful for integrating different algorithms proposed to solve the same clinical task.

Methods

The proposed approach was developed starting from a public database consisting of radiomic features extracted from CT images relat

SUBMITTER: Fanizzi A 

PROVIDER: S-EPMC11386419 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature