Article Text

Original research
Novel multiclass classification machine learning approach for the early-stage classification of systemic autoimmune rheumatic diseases
  1. Yun Wang,
  2. Wei Wei,
  3. Renren Ouyang,
  4. Rujia Chen,
  5. Ting Wang,
  6. Xu Yuan,
  7. Feng Wang,
  8. Hongyan Hou and
  9. Shiji Wu
  1. Department of Laboratory Medicine, Tongji Hospital of Tongji Medical College of Huazhong University of Science and Technology, Wuhan, Hubei, China
  1. Correspondence to Dr Shiji Wu; wilson547{at}; Dr Hongyan Hou; tjhouhongyan{at}; Dr Feng Wang; fengwang{at}


Objective Systemic autoimmune rheumatic diseases (SARDs) encompass a diverse group of complex conditions with overlapping clinical features, making accurate diagnosis challenging. This study aims to develop a multiclass machine learning (ML) model for early-stage SARDs classification using accessible laboratory indicators.

Methods A total of 925 SARDs patients were included, categorised into SLE, Sjögren’s syndrome (SS) and inflammatory myositis (IM). Clinical characteristics and laboratory markers were collected and nine key indicators, including anti-dsDNA, anti-SS-A60, anti-Sm/nRNP, antichromatin, anti-dsDNA (indirect immunofluorescence assay), haemoglobin (Hb), platelet, neutrophil percentage and cytoplasmic patterns (AC-19, AC-20), were selected for model building. Various ML algorithms were used to construct a tripartite classification ML model.

Results Patients were divided into two cohorts, cohort 1 was used to construct a tripartite classification model. Among models assessed, the random forest (RF) model demonstrated superior performance in distinguishing SLE, IM and SS (with area under curve=0.953, 0.903 and 0.836; accuracy= 0.892, 0.869 and 0.857; sensitivity= 0.890, 0.868 and 0.795; specificity= 0.910, 0.836 and 0.748; positive predictive value=0.922, 0.727 and 0.663; and negative predictive value= 0.854, 0.915 and 0.879). The RF model excelled in classifying SLE (precision=0.930, recall=0.985, F1 score=0.957). For IM and SS, RF model outcomes were (precision=0.793, 0.950; recall=0.920, 0.679; F1 score=0.852, 0.792). Cohort 2 served as an external validation set, achieving an overall accuracy of 87.3%. Individual classification performances for SLE, SS and IM were excellent, with precision, recall and F1 scores specified. SHAP analysis highlighted significant contributions from antibody profiles.

Conclusion This pioneering multiclass ML model, using basic laboratory indicators, enhances clinical feasibility and demonstrates promising potential for SARDs classification. The collaboration of clinical expertise and ML offers a nuanced approach to SARDs classification, with potential for enhanced patient care.

  • Autoimmune Diseases
  • Autoimmunity
  • Lupus Erythematosus, Systemic

Data availability statement

All data relevant to the study are included in the article or uploaded as online supplemental information.

This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See:

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  • Machine learning (ML) models have been successfully applied to various aspects of rheumatology, but the utilisation of ML for the differential classification of systemic autoimmune rheumatic diseases (SARDs), particularly during the early stages, remains a relatively underexplored area of research.


  • This study introduced the application of a multiclass ML model in the realm of SARDs demonstrates the feasibility and effectiveness of employing an ML model based on basic laboratory indicators for the accurate multiclass classification of these diseases.


  • The multiclass ML model promises to provide a more nuanced and complex classification of SARDs, ultimately paving the way for enhanced patient care and improved disease management.


Systemic autoimmune rheumatic diseases (SARDs) encompass a diverse group of chronic and complex conditions characterised by aberrant immune responses leading to self-directed tissue and organ damage.1 These diseases, including SLE, rheumatoid arthritis (RA), systemic sclerosis, Sjögren’s syndrome (SS), mixed connective tissue disease and inflammatory myositis (IM), among others, share a common autoimmune pathogenesis while exhibiting similar or distinct clinical presentations and affecting various organs.2 The classification of SARDs is of paramount importance in clinical practice, research and drug development, as it enables the inclusion of homogeneous patient populations and facilitates the evaluation of disease outcomes and therapeutic interventions.3 However, prompt and accurate diagnosis of SARDs remains a significant challenge in clinical practice due to the overlapping clinical features shared among various autoimmune disorders.4

The absence of definitive diagnostic criteria necessitates the reliance on classification criteria, developed primarily for research purposes, as diagnostic aids.5 Among the widely recognised classification criteria, two of the most prominent are the American College of Rheumatology (ACR) and the European League Against Rheumatism (EULAR) criteria. These criteria have been refined over the years through iterative processes involving expert consensus and systematic data analysis, making them primary tools for classifying diseases such as RA,6 SLE,7 8 SS9 and IM.10 Recently, efforts have been made to develop more sensitive and specific classification criteria to capture early and milder forms of SARDs. This led to the development of the Systemic Lupus International Collaborating Clinics (SLICC) criteria for SLE11 and the EULAR/ACR 2019 criteria,12 which combines elements from both organisations for SLE classification. Despite these advancements, challenges remain in the classification of SARDs, particularly in distinguishing between diseases with overlapping clinical features and in identifying rare and atypical cases.13 Consequently, delayed diagnosis and treatment initiation can result in increased disease activity, organ damage and poor patient outcomes.

Artificial intelligence (AI) and machine learning (ML) are increasingly recognised as powerful tools capable of handling complex medical tasks.14 ML offers distinct advantages over conventionally programmed strategies, particularly in handling complex multidimensional data by identifying latent relationships that may have escaped prior recognition.15 ML models, trained on diverse medical and biological data, have been successfully applied to various aspects of rheumatology,16 including molecular classification of SS,17 IM18 and RA,19 assessment of disease activity,20 response to treatment21 and prediction of disease outcomes22 or mortality.23 The capacity of ML to comprehensively analyse extensive and intricate clinical datasets holds immense potential. Nevertheless, its seamless translation into clinical practice within the realm of rheumatology is still at a formative stage.24 Moreover, the utilisation of ML for the differential diagnosis of SARDs, particularly during the early stages,25–28 remains a relatively underexplored area of research. Specifically, there is a dearth of investigations focusing on the development of ML models that rely on straightforward and readily available objective laboratory indicators, such as ANA profiles, to accurately identify common SARDs in their early stages (within 2 year of disease onset).

Materials and methods

Study design and study population cohorts

We conducted a single-centre observational retrospective study that included all study subjects from the Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology.

A total of 925 patient initially diagnosed with SLE (n=519), SS (n=163) and IM (n=243) were included in this study from October 2017 to June 2023. Cohort 1 (model building and internal validation): patients with SLE, IM and SS who had their initial consultations from October 2017 to May 2022 were included to establish a classification model for distinguishing between IM, SLE and SS. Cohort 2 (external validation): patients with IM, SLE and SS who were first seen between June 2022 and June 2023 were included for external validation. All patients were diagnosed for the first time within 2 years of symptom onset. The major clinical symptoms included dizziness and fatigue, cough, joint pain (synovitis involving two or more joints, characterised by swelling or effusion or tenderness in two or more joints and 30 min or more of morning stiffness), oedema (extremity oedema, eyelid oedema, abdominal oedema and pulmonary oedema etc), proteinuria (urine protein>0.5 g/24 hours), fever (>38.2°C) and rash (such as butterfly rash, subacute cutaneous lupus erythematosus, discoid lupus erythematosus, purpura etc). None of these patients had received specific treatment or previous immunosuppressive drugs. The diagnosis of SS was made according to the criteria of the 2016 ACR/EULAR Classification Criteria,9 while the diagnosis of IM was based on the criteria of the 2017 EULAR/ACR classification criteria for adult and juvenile idiopathic inflammatory myopathies.10 The diagnosis of SLE before 2019 followed the 2012 SLICC Classification Criteria for SLE,11 and from 2019 to 2023, the criteria of the 2019 EULAR/ACR SLE classification criteria12 were employed. Exclusion criteria encompassed patients who were HIV or hepatitis C virus positive and malignancies. The patients who may satisfy multiple classification criteria were also excluded.

Data cleaning

The basic information of the study subjects (age, gender, underlying disease and medical history) and the results of all first laboratory tests (antinuclear antibodies, anti-dsDNA (indirect immunofluorescence assay (IIFA)), routine blood, liver function, kidney function, ion, lipid, glucose, cardiac protein and cardiac enzyme, etc) were collected in the medical record system within 72 hours after admission. The patient ID number is used as the unique identifier. Serum ANA profiles levels were measured using BioPlex 2200 System. The IIFA for the detection of ANA and anti-dsDNA was performed using the EUROPattern Computer-aided immunofluorescence microscopy (EPA). Routine blood levels were detected by the Sysmex instrument. Liver function and kidney function levels were measured using the Roche Cobas e701 Automatic Electrochemiluminescence Immunoassay System. Test data with missing rates >30% were excluded.

Model fitting and evaluation

The flow chart for building a model using ML algorithms is shown in figure 1. ML models were constructed using the variables that exhibited statistically significant differences among three groups, as well as variables that have been highlighted in existing literature. To mitigate multicollinearity, correlation analysis was performed to identify indicators with high correlations. Subsequently, ROC analysis, single-factor analysis or multifactor regression were employed to identify the distinctive features (online supplemental table 1).

Supplemental material

Figure 1

Flowchart of machine learning to build the diagnosis model. ACC, accuracy; IM, inflammatory myositis; SS, Sjögren’s syndrome.

Cohort 1 was randomly divided into two subsets: 85% for training and 15% for testing. The training set uses fivefold cross-validation of the entire dataset into fivefolds, using four of the folds as the training set to train the model, and the remaining one fold as the internal validation set to score the model, and repeating the above process five times. We compare the early classification models built by Extreme Gradient Boosting (XgBoost), Logistic Regression, Light Gradient Boosting Machine (LightGBM) and RF, and select the best performing ML algorithms and early classification models (table 1) in the validation set. Cohort 2 performs external validation of the model. The prediction models were constructed using the Beckman Coulter DxAI platform ( Outcome was categorised into three categories (SLE, IM and SS). RF model was selected to construct classification models. The RF model is an ensemble learning algorithm that combines multiple decision trees to improve classification accuracy and reduce overfitting by using random feature selection and voting/averaging mechanisms. It is widely used for classification and regression tasks. Shapley additive explanation (SHAP) analysis was used to elucidate the significance and contributions of features within the RF model. To evaluate the performance of the models, accuracy was calculated to determine the model’s performance. The macro and weighted precision, recall, F1 were calculated for the three classes.

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Table 1

Results of the different machine learning models

Statistical analysis

Continuous variables were presented as either mean±SD or median (IQ)), depending on the data distribution. The Student’s t-test, Mann-Whitney U test or one-way analysis of variance test was used to compare the variables between groups, as appropriate. Categorical variables were compared using the χ2 test or Fisher’s exact test based on the sample size and expected cell frequencies. Receiver operating characteristic (ROC) curve analysis was performed to identify the optimal cut-off values for parameters, aiming to achieve the highest sensitivity and specificity. Lasso regression was employed for feature variable selection using the R package ‘Lasso2’. To explore differences between groups based on laboratory indicators, t-distributed stochastic neighbour embedding analysis was conducted using the R package ‘Rtsne’, while heatmaps were generated using the R package ‘pheatmap’. Correlations between indicators were analysed and correlation matrix visualisation was performed using the R package ‘corrplot’. Principal component analysis (PCA) was executed using the R package ‘PCA’ to analyse the associations between categorical variables. These tests were employed to analyse the associations between categorical variables in the study. Statistical analyses were performed using GraphPad Prism V.9.0 (San Diego, California, USA) and SPSS V.22.0. Statistical significance was determined as p<0.05.


Basic characteristics of included cohorts

Comparing the IM, SLE and SS groups within cohort 1, no significant differences were identified with regard to age, sex, joint pain and fever. The predominant clinical features in IM, SLE and SS groups were dizziness and fatigue (35.48%), oedema (35.78%) and fever (23.74%), respectively. The clinical symptoms observed in cohort 2 closely paralleled those identified in the three groups within cohort 1 (table 2). Certain symptoms such as proteinuria and oedema were notably higher in SLE compared with SS and IM. Joint pain, a hallmark symptom of many rheumatic diseases, displayed varying prevalence rates, further underscoring the intricate nature of symptomatology in SARDs. Additionally, symptoms that might not immediately seem directly related to rheumatic diseases, such as cough and fever, exhibited noteworthy prevalence rates, highlighting the multisystemic nature of these conditions.

Table 2

Clinical baseline characteristics

Significantly differences of ANA patterns and routine laboratory indicators were observed among SLE, SS and IM groups, including blood routine, liver function, complements and immunoglobulin (online supplemental table 2). Further insights into the ANA profiles are depicted (figure 2A–L). Markers specific to SLE, such as anti-dsDNA, antichromatin, anti-Sm and antiribosomal P antibodies, displayed significantly higher concentrations in the SLE group compared with the SS and IM groups. SLE-associated markers such as anti-Sm/nRNP and anti-RNP A/68 also exhibited higher concentrations in the SLE group. Notably, the IM-specific antibody anti-Jo-1 showed the highest levels in the IM group. The antibody anti-SS-B, more closely related to SS, displayed the highest concentration in the SS group. Moreover, the antibody anti-SS-A60, highly associated with both SLE and SS, demonstrated no distinction between the two groups but was significantly elevated in comparison to the IM group. However, the major clinical symptoms and routine laboratory indicators did not exhibit a clustered distribution across the three disease groups (online supplemental figure 1A). With the addition of specific laboratory indicators (ANA patterns and ANA profiles), the distinction improved to some extent, although there still remained significant overlap (online supplemental figure 1B).

Supplemental material

Supplemental material

Figure 2

The characteristics of antinuclear antibody profiles in patients newly diagnosed with SLE, SS and IM (A–L). The data were presented as mean with SD. Blue circle points represent SLE, orange circle points represent SS and green circle points represent IM. *P<0.05, ***p<0.001. IM, inflammatory myositis; SS, Sjögren’s syndrome.

Establishment of machine learning model

Following a rigorous feature selection strategy (as described in the Materials and methods section), we identified the most informative features that could serve as potential biomarkers for distinguishing between IM, SLE and SS. Ultimately, nine key features, anti-dsDNA, anti-SS-A60, anti-Sm/nRNP, antichromatin, anti-dsDNA (IIFA), haemoglobin (Hb) levels, platelet count (PLT), neutrophil percentage (NEUT%) and cytoplasmic patterns (AC-19, AC-20), were finally selected to build the ML model. Based on the afore-mentioned indicators, we opted for the top-performing model using the RF classifier model for its superior performance when distinguishing between SLE, IM and SS. For the predictive classification of SLE, the RF model demonstrated an area under curve (AUC) value of 0.953 (95% CI: 0.932 to 0.974), an accuracy of 0.892 (95% CI: 0.889 to 0.894), sensitivity of 0.890 (95% CI: 0.854 to 0.926), specificity of 0.910 (95% CI: 0.885 to 0.935), a positive predictive value (PPV) of 0.922 (95% CI: 0.887 to 0.958) and a negative predictive value (NPV) of 0.854 (95% CI: 0.821 to 0.887). For the predictive classification of IM and SS, the RF model yielded AUC values of 0.903 (95% CI: 0.866 to 0.940) and 0.836 (95% CI: 0.782 to 0.890), respectively, along with accuracy values of 0.869 and 0.857. The model also exhibited sensitivity values of 0.868 and 0.795, specificity values of 0.836 and 0.748, PPVs of 0.727 and 0.663 and NPVs of 0.915 and 0.879 (table 1). We trained and evaluated RF models to classify the three classes of our cohorts (IM, SLE and SS). The results encompassing weighted precision, recall, weighted F1 scores and support for our predictive models are comprehensively presented in table 3. In the training set, weighted precision, recall and weighted F1 scores for all three groups were >0.9. On evaluating the testing set, the RF model demonstrated its optimal diagnostic prowess in identifying SLE cases (precision=0.930, recall=0.985, F1 score=0.957). When predicting IM and SS cases, the RF model yielded the outcomes (precision=0.793, 0.950, recall=0.920, 0.679, F1 score=0.852, 0.792), respectively. The comprehensive confusion matrix for both the training and validation sets is illustrated in figure 3A. Further elucidating the model’s intricacies, the SHAP values are presented in figure 3B–C. Notably, antichromatin, anti-dsDNA and anti-SS-A60 emerged as pivotal contributors significantly influencing the model’s output magnitude, while cytoplasmic patterns (AC-19, AC-20) exhibited the lowest SHAP value, implying relatively lesser impact on the model’s predictions. In the PCA biplot graph, the contribution of these nine indicators to the classification of SLE, SS and IM can also be observed (online supplemental figure 2). Notably, anti-dsDNA, anti-Sm/nRNP, antichromatin and anti-dsDNA (IIFA) antibodies are oriented towards SLE and exhibit relatively significant contributions. Conversely, Hb and PLT predominantly align with the IM category, showcasing substantial contributions. NEUT% lies between SLE and IM, suggesting a middle ground. Similar to the SHAP graph, cytoplasmic patterns (AC-19, AC-20) demonstrate the least contribution. The presence of anti-SS-A antibodies between SLE and SS categories is not unexpected, given their intermediate positioning. The comprehensive evaluation of bias and model risks, conducted through the PROBAST tool, can be found in online supplemental table 3.

Supplemental material

Supplemental material

Table 3

Performance of the built machine learning model

Figure 3

In the internal testing set, the random forest (RF) model built by nine key features (antichromatin, anti-dsDNA, anti-SS-A60, neutrophil percentage, anti-dsDNA IFT, platelet count, anti-Sm/nRNP, haemoglobin level and cytoplasmic (AC-19, AC-20)). (A) The comprehensive confusion matrix for the training set. (B) The comprehensive confusion matrix for the testing set. (C) The SHAP values. Each feature’s SHAP value indicates its contribution to the model’s prediction. (D) The weights of variables importance of top nine features. IM, inflammatory myositis; SHAP, Shapley additive explanation; SS, Sjögren’s syndrome.

External verification

Using cohort 2 as an external verification set, our model’s performance was evaluated, yielding an overall diagnostic accuracy of 87.3% (figure 4). Specifically, when classifying individual diseases, the model exhibited its highest diagnostic performance in correctly identifying cases of SLE, with a precision of 0.882, recall of 0.971 and an F1 score of 0.924. In terms of differentiating between IM and SS cases, the RF model demonstrated precision rates of 0.897 and 0.750, along with recall rates of 0.912 and 0.500, respectively. These outcomes translated to F1 scores of 0.904 for IM and 0.600 for SS (table 4).

Figure 4

Confusion matrix for external validation of the diagnosis model. IM, inflammatory myositis; SS, Sjögren’s syndrome.

Table 4

Confusion matrix for external validation of the diagnosis model


The clinical presentation of SARDs is characterised by a diverse array of symptoms.2 The intricate interplay between diverse symptoms in each SARD highlights the challenges faced by clinicians in distinguishing these conditions from one another, particularly considering the potential for overlapping symptoms.29 30 Within clinical practice, skilled physicians can often arrive at a diagnosis of SARDs even with the presence of a limited number of highly informative manifestations in an individual. For instance, the presence of the classic malar rash coupled with anti-dsDNA can lead to a diagnosis of SLE. However, this diagnostic acumen is a manifestation of clinical expertise cultivated through experience.31 The data emphasise the importance of developing advanced diagnostic tools, such as ML models, to aid in the accurate and timely differentiation of SARDs.

ML, a computational analytical approach, is gaining rapid prominence in the realm of biomedicine.32 33 In the context of rheumatology, the integration of ML is witnessing a gradual upsurge, with numerous studies leveraging ML techniques to classify patients with SARDs based on diverse data sources encompassing medical records,34 35 imaging data,36 biometric measurements37 and gene expression profiles.18 20 38 The modelling indicators of these ML models primarily encompass clinical symptoms, which are relatively subjective, as well as a diverse array of complex laboratory indicators, and even biologically intricate indicators that are challenging to obtain, such as genetic polymorphisms. However, comparatively simple and easily accessible laboratory indicators, such as blood routine, and crucial autoantibodies of significant relevance in SARDs diagnosis, such as ANA profiles, are seldom incorporated into consideration. This limitation to some extent impacts the applicability of these models in clinical contexts.

In this study, we present a novel ML framework that is based on readily accessible and objective laboratory indicators, notably the ANA profile and blood routine, tailored for the early-stage classification and diagnosis of three SARDs (SLE, SS and IM). This approach holds the potential to surmount the diagnostic challenges posed by the overlapping clinical presentations commonly observed in SARDs. Through an exhaustive process encompassing techniques such as differential analysis, feature selection, weight analysis, we ultimately identified laboratory indicators anti-dsDNA, anti-SS-A60, anti-Sm/nRNP, antichromatin, anti-dsDNA (IIFA), Hb, PLT, NEUT% and cytoplasmic patterns (AC-19, AC-20) for inclusion as modelling factors. The inclusion of ANA profiles as modelling indicators is expected. It is widely acknowledged that certain antibodies play pivotal roles in classifying SARDs.39 For instance, the presence of anti-dsDNA and anti-Sm antibodies contributes to the classification criteria of SLE,12 while the detection of anti-SS-A60 antibodies is significant for diagnosing SS.9 Moreover, the prevalence of antichromatin antibodies is strongly associated with lupus nephritis.40 Additionally, different methods for detecting anti-dsDNA antibodies may yield varying diagnostic performance. Currently, the most specific method acknowledged for diagnosing SLE is the IIFA.41 Some indicators within the classification criteria, such as complement C3 and C4, were unexpectedly excluded, potentially due to their lack of specificity. Even more unexpectedly, certain highly specific antibodies such as anti-Jo-1, anti-Sm and anti-RibP antibodies were not included, possibly due to their low positive rates in clinic.41 The IIFA on HEp-2 cells is widely used for detection of ANA. Fluorescence patterns may also reveal clinically relevant information. Cytoplasmic patterns (AC-19, AC-20) are associated with the distinct anti-tRNA synthetase antibodies, which are significant relevant antibodies in the context of IM.42 Considering the comprehensive impact of SARDs on the blood system, factors such as Hb, PLT and NEUT% hold significant clinical significance. Reduced haemoglobin levels, known as anaemia, and thrombocytopenia, characterised by low PLT counts, are commonly observed in individuals with SARDs, arising from factors such as chronic inflammation, bone marrow suppression, renal involvement, immune-mediated destruction or impaired PLT production. Leucopenia is also a common symptom of SARDs and is one of the classification criteria for SLE. However, surprisingly, what was selected is the NEUT% rather than the total white blood cell counts. Neutrophils play a central role in the immune response, and alterations in NEUT%s can reflect the immune dysregulation present in SARDs. In recent years, emerging evidence highlights the potential of SLE-derived low-density granulocytes to contribute to vascular damage, heightened type I interferon synthesis, increased cell death and enhanced extracellular trap formation, all potentially significant in SLE pathogenesis and autoimmunity induction.43

In contrast to the prevalent binary classification methods employed by existing ML models, which primarily categorise outcomes as either ‘yes’ or ‘no’, our model represents a pioneering effort in the realm of multiclass classification, offering a novel approach where a single model can provide the risk probabilities associated with three distinct disease types for each patient which enhance clinical operability and applicability. We opted for the top-performing model using the RF Classifier model for its superior performance. The model demonstrated an accuracy of 90.0% when tested on a subset of 120 patients, randomly selected for evaluation. Notably, the accuracy rates for distinct SARDs subtypes were as follows: 98.5% (66/67) for SLE, 67.9% (19/28) for SS and 92.0% (23/25) for IM, respectively. Finally, we conducted a performance validation of the model in a separate cohort comprising 150 newly diagnosed patients, and the results consistently demonstrated a high accuracy rate (87.3%). Compared with other diagnostic models, our model demonstrates a comparable high performance. For example, Burlina et al achieved an 86.6% accuracy in diagnosing IM through AI learning of muscle ultrasound images.36 Pinal-Fernandez et al further discovered that dermatomyositis, antisynthetase syndrome, immune-mediated necrotising myopathy and inclusion body myositis can be distinguished based on their unique gene expression patterns by applying ML algorithms to muscle biopsy transcriptomic data.18 However, this study’s outcomes generated five different ML models, a limitation that significantly constrains its practicality in clinical settings. Dros et al developed an ML model based on routine healthcare data, achieving an 84.0% diagnostic accuracy for primary Sjogren’s syndrome (pSS).34 Another study, using a set of 14 signature genes that play pivotal roles in transcription regulation and disease progression in pSS, achieved an 83.2% accuracy for pSS diagnosis.38 Our model demonstrates a lower classification accuracy for SS compared with the outcomes of these two studies. This disparity could be attributed to variations in the composition of patient groups, differences in the chosen modelling indicators, and other relevant factors. Notably, our study includes a smaller proportion of SS patients in comparison to those with SLE and IM. As a result, the selected modelling indicators, aside from anti-SS-A60, contribute less significantly to the accurate classification of SS in our model. In a study by Adamichou et al, an ML model relying on 14 indicators encompassing a diverse range of clinical symptoms and laboratory parameters, including complement C3 and C4 levels, achieved a diagnostic accuracy of 94.2% for SLE.16 The research is one of the few studies that employs readily available and simple indicators for modelling, aiming to maximise the clinical applicability. However, there exists a substantial disparity in the modelling indicators used between our study and theirs. This divergence could potentially be attributed to the differences in the selected patient cohorts, as distinct patient populations often exhibit significant variations in their characteristic features, subsequently influencing the extracted feature indicators.

Our study brings forth a pioneering approach in the field of SARDs classification by introducing a novel multiclass ML model. This innovative model employs basic laboratory indicators, such as ANA profiles and blood routine results, to effectively classify patients into three distinct disease types. This approach not only offers a simplified classification process but also enhances the clinical feasibility of the model, aligning with real-world medical practices. However, it is important to acknowledge the limitation posed by our patient cohort’s predominantly Han ethnicity composition, which restricts the generalisability of our findings to diverse ethnic groups. Second, in our study, autoantibodies are treated as quantitative data. It is important to note that values of antibody levels can vary significantly between different commercial assays, which may impact the generalisability of the model. The third concern is that many of our modelling indicators, such as anti-dsDNA antibodies, are autoantibodies already used in clinical classification, posing a significant risk of circular reasoning. This is also a key reason for the high-risk assessment of outcome and predictor association in the bias risk evaluation using PROBAST. However, it is crucial to clarify that the primary aim of our model is to extract information from rich laboratory parameters through ML, rather than simply reproducing clinical classification criteria. We will emphasise more clearly that, while some antibodies may be relevant to current classification criteria, the value of the model lies in the potential associations it learns from complex data. Additionally, the relatively lower accuracy in classifying SS patients highlights the need for larger sample sizes to improve SS classification performance and ensure comprehensive applicability. Despite these limitations, our study’s promising implications for accurate and expedited SARDs diagnosis underscore the necessity for further research and validation across diverse patient populations.

In conclusion, our study introduces the application of a multiclass ML model in the realm of SARDs, demonstrates the feasibility and effectiveness of employing an ML model based on basic laboratory indicators for the accurate multiclass classification of these diseases. Certainly, the wisdom and experience of clinical physicians remain essential. The amalgamation of clinical acumen with ML-driven insights promises a more nuanced and sophisticated approach to diagnosing SARDs, ultimately paving the way for enhanced patient care and improved disease management.

Data availability statement

All data relevant to the study are included in the article or uploaded as online supplemental information.

Ethics statements

Patient consent for publication

Ethics approval

The study was approved by the ethics committee of the Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology (TJ-IRB202308129).


Supplementary materials


  • YW, WW and RO contributed equally.

  • Contributors YW, RO and WW collected data from patient medical charts and also performed data entry. SW designed and implemented the machine learning (ML) methodology, constructed and evaluated the ML models and drafted the relevant methodology sections on feature selection, model construction, evaluation and statistical analysis. HH organised the RedCap database. SW, RC, XY and TW performed statistical analyses and drafted the manuscript. FW and HH helped revise the manuscript. SW is responsible for the overall content as guarantor.

  • Funding The study was supported by grants from Hubei Provincial Health Commission (WJ2023M010).

  • Competing interests None declared.

  • Provenance and peer review Not commissioned; externally peer reviewed.

  • Supplemental material This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.