Systemic lupus erythematosus with high disease activity identification based on machine learning

Wang, Da-Cheng; Xu, Wang-Dong; Qin, Zhen; Fu, Lu; Lan, You-Yu; Liu, Xiao-Yan; Huang, An-Fang

doi:10.1007/s00011-023-01793-1

Inflamm. Res.

2023

DOI: 10.1007/s00011-023-01793-1

|View full text |Cite

Systemic lupus erythematosus with high disease activity identification based on machine learning

Da-Cheng Wang,

Wang-Dong Xu,

Zhen Qin

et al.

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

Publication Types

Select...

Article3

Relationship

Self Cite0

Independent3

Authors

Journals

Cited by 3 publications

References 50 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Trends in the prevalence of osteoporosis and effects of heavy metal exposure using interpretable machine learning

Xiao,

Liang,

et al. 2024

Ecotoxicology and Environmental Safety

View full text Add to dashboard Cite

Trends in the prevalence of osteoporosis and effects of heavy metal exposure using interpretable machine learning

Xiao,

Liang,

et al. 2024

Ecotoxicology and Environmental Safety

View full text Add to dashboard Cite

Systemic lupus in the era of machine learning medicine

Zhan,

Buhler,

Chen

et al. 2024

Lupus Sci Med

View full text Add to dashboard Cite

Artificial intelligence and machine learning applications are emerging as transformative technologies in medicine. With greater access to a diverse range of big datasets, researchers are turning to these powerful techniques for data analysis. Machine learning can reveal patterns and interactions between variables in large and complex datasets more accurately and efficiently than traditional statistical methods. Machine learning approaches open new possibilities for studying SLE, a multifactorial, highly heterogeneous and complex disease. Here, we discuss how machine learning methods are rapidly being integrated into the field of SLE research. Recent reports have focused on building prediction models and/or identifying novel biomarkers using both supervised and unsupervised techniques for understanding disease pathogenesis, early diagnosis and prognosis of disease. In this review, we will provide an overview of machine learning techniques to discuss current gaps, challenges and opportunities for SLE studies. External validation of most prediction models is still needed before clinical adoption. Utilisation of deep learning models, access to alternative sources of health data and increased awareness of the ethics, governance and regulations surrounding the use of artificial intelligence in medicine will help propel this exciting field forward.

show abstract

A Coyote-Inspired Approach for Systemic Lupus Erythematosus Prediction Using Neural Networks

Mummaneni,

Dodda,

Ginjupalli

2024

IAPGOS

View full text Add to dashboard Cite

Systemic Lupus Erythematosus (SLE) is a complicated autoimmune disease that can present with a variety of clinical symptoms, making precise prognosis difficult. Because SLE has a wide range of symptoms and may overlap with other autoimmune and inflammatory disorders, making a diagnosis can be challenging. This study creates a precise and accurate model for the prediction of SLE using the GEO dataset. For cost-effective data collection and analysis, feature selection might be essential in some applications, particularly in healthcare and scientific research. The strength of Artificial Neural Networks (ANN) for Systemic Lupus Erythematosus prediction and the Coyote Optimization Algorithm (COA) for feature selection are combined in this study. The COA is an optimization method influenced by nature and coyote hunting behavior. This study attempts to improve the effectiveness of subsequent predictive modeling by using COA to identify a subset of significant features from high-dimensional datasets linked to SLE. A Multi-layer Feed-forward Neural Network, a potent machine learning architecture renowned for its capacity to discover complex patterns and correlations within data, is then given the chosen features. Because the neural network is built to capture SLE's intricate and non-linear structure, it offers a reliable foundation for precise classification and prediction. The accuracy of the COA-ANN model was 99.6%.

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Systemic lupus erythematosus with high disease activity identification based on machine learning

Cited by 3 publications

References 50 publications

Trends in the prevalence of osteoporosis and effects of heavy metal exposure using interpretable machine learning

Trends in the prevalence of osteoporosis and effects of heavy metal exposure using interpretable machine learning

Systemic lupus in the era of machine learning medicine

A Coyote-Inspired Approach for Systemic Lupus Erythematosus Prediction Using Neural Networks

Contact Info

Product

Resources

About