Impact of the Choice of Cross-Validation Techniques on the Results of Machine Learning-Based Diagnostic Applications.

ABSTRACT:

Objective

With advances in data availability and computing capabilities, artificial intelligence and machine learning technologies have evolved rapidly in recent years. Researchers have taken advantage of these developments in healthcare informatics and created reliable tools to predict or classify diseases using machine learning-based algorithms. To correctly quantify the performance of those algorithms, the standard approach is to use cross-validation, where the algorithm is trained on a training set, and its performance is measured on a validation set. Both datasets should be subject-independent to simulate the expected behavior of a clinical study. This study compares two cross-validation strategies, the subject-wise and the record-wise techniques; the subject-wise strategy cor

SUBMITTER: Tougui I 

PROVIDER: S-EPMC8369053 | biostudies-literature | 2021 Jul

REPOSITORIES: biostudies-literature