A Literature Review on Disease Detection with Automated Machine Learning

Abstract

This study reviews disease detection with Automated Machine Learning (AutoML), aiming to identify gaps and evaluate AutoML's impact in this field. In this study, seven review articles published in Q1- or Q2-quartile journals between 2020 and 2025 were analyzed. The reviews were assessed using ten academic criteria, covering AutoML performance, data strategies, feature techniques, noise reduction, model selection, training/testing methods, and frameworks for disease detection. Additionally, test reliability, patient selection, reference standards, and application processes were evaluated with the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) tool. A literature review was conducted using 11 different databases; however, due to limited functionality in four of them, the research primarily relied on seven digital databases, which initially yielded 552 studies. The study selection and screening processes were performed in accordance with the Preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines. Next, 40 studies published outside the 2020-2025 period were removed, followed by the exclusion of 117 studies that were not journal articles. An additional 145 studies were eliminated because they were reviews, books, conference proceedings, posters, editorial notes, etc., and seven studies were excluded as they did not pertain to human diseases. After these elimination processes, 243 articles remained for full-text review. Out of these, 214 articles were read in full and assessed for relevance, leading to 29 articles deemed suitable for inclusion in this review on disease detection using AutoML. After removing five duplicate articles, a final total of 24 studies were included in the review. The research questions of the study include questions such as which disease detection models AutoML methods are preferred more, the input features and data sets used, the effects of feature extraction and selection methods on model performance, how often noise reduction methods are used in disease data, and what the AutoML model evaluation metrics are. The results show that AutoML methods are effectively used on disease detection and that different AutoML techniques, data sets, and model selection processes make significant contributions to success. This review provides an important resource for making AutoML applications for disease detection more efficient and for eliminating the deficiencies in the literature.