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. 2022 Jan 25:2022:9273641.
doi: 10.1155/2022/9273641. eCollection 2022.

Application of Machine Learning in Rheumatic Immune Diseases

Yuan Li  1 , Linru Zhao  1
Affiliations

Application of Machine Learning in Rheumatic Immune Diseases

Yuan Li et al. J Healthc Eng. .

Retraction in

Abstract

People are paying greater attention to their personal health as society develops and progresses, and rheumatic immunological disorders have become a serious concern that affects human health. As a result, research on a stable, trustworthy, and effective auxiliary diagnostic method for rheumatic immune disorders is critical. Machine learning overcomes the inefficiencies and volatility of human data processing, ushering in a revolution in artificial intelligence research. With the use of big data, machine learning-based application research on rheumatic immunological disorders has already demonstrated detection abilities that are on par with or better than those of medical professionals. Artificial intelligence systems are now being applied in the field of rheumatic immune disorders, with an emphasis on the identification of patient joint images. This article focuses on the use of machine learning algorithms in the diagnosis of rheumatic illnesses, as well as the practical implications of disease-assisted diagnosis systems and intelligent medical diagnosis. This article focuses on three common machine learning algorithms for research and debate: logistic regression, support vector machines, and adaptive boosting techniques. The three algorithms are used to build diagnostic models based on rheumatic illness data, and the performance of each model is assessed. According to a thorough analysis of the assessment data, the diagnostic model based on the limit gradient boosting method has the best resilience. This article presents machine learning's use and advancement in rheumatic immunological disorders, as well as new ideas for investigating more appropriate and efficient diagnostic and treatment techniques.

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Conflict of interest statement

The author declares no conflicts of interest.

Figures

Figure 1
Figure 1
The relationship between the average accuracy and the threshold.
Figure 2
Figure 2
The relationship between the error rate and the number of classifiers.
Figure 3
Figure 3
Comparison between different diagnostic models w.r.t. accuracy.
Figure 4
Figure 4
Comparison between different diagnostic models w.r.t. precision.
Figure 5
Figure 5
Comparison between different diagnostic models w.r.t. recall.
See this image and copyright information in PMC

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