Exploring machine learning algorithms in sickle cell disease patient data: a systematic review

Machado, Tiago Fernandes, das Chagas Barros Neto, Francisco, de Souza Gonçalves, Marilda, Barbosa, Cynara Gomes and Barreto, Marcos Ennes ORCID: 0000-0002-7818-1855 (2024) Exploring machine learning algorithms in sickle cell disease patient data: a systematic review. PLOS ONE, 19 (11). ISSN 1932-6203

Text (journal.pone.0313315) - Published Version Available under License Creative Commons Attribution. Download (1MB)

• Scopus publication

Abstract

This systematic review explores the application of machine learning (ML) algorithms in sickle cell disease (SCD), focusing on diagnosis and several clinical characteristics, such as early detection of organ failure, identification of drug dosage, and classification of pain intensity. A comprehensive analysis of recent studies reveals promising results in using ML techniques for diagnosing and monitoring SCD. The review covers various ML algorithms, including Multilayer Perceptron, Support Vector Machine, Random Forest, Logistic Regression, Long short-term memory, Extreme Learning Machines, Convolutional Neural Networks, and Transfer Learning methods. Despite significant advances, challenges such as limited dataset sizes, interpretability concerns, and risks of overfitting are identified in studies. Future research directions entail addressing these limitations by harnessing larger and more representative datasets, enhancing model interpretability, and exploring advanced ML techniques like deep learning. Overall, this review underscores the transformative potential of ML in increasing the diagnosis, monitoring and define prognosis of sickle cell disease while also highlighting the need for further investigation in the field.

Item Type:	Article
Additional Information:	© 2024 The Authors
Divisions:	Statistics
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science R Medicine > RA Public aspects of medicine
Date Deposited:	21 Nov 2024 12:33
Last Modified:	20 Dec 2024 00:58
URI:	http://eprints.lse.ac.uk/id/eprint/126129

Actions (login required)

View Item