Novel multiclass classification machine learning approach for the early-stage classification of systemic autoimmune rheumatic diseases
© Author(s) (or their employer(s)) 2024. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ..
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.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2024 |
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| Erschienen: |
2024 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:11 |
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| Enthalten in: |
Lupus science & medicine - 11(2024), 1 vom: 31. Jan. |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Wang, Yun [VerfasserIn] |
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| Links: |
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| Themen: |
Antibodies, Antinuclear |
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| Anmerkungen: |
Date Completed 05.02.2024 Date Revised 05.02.2024 published: Electronic Citation Status MEDLINE |
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| doi: |
10.1136/lupus-2023-001125 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| 245 | 1 | 0 | |a Novel multiclass classification machine learning approach for the early-stage classification of systemic autoimmune rheumatic diseases |
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| 500 | |a published: Electronic | ||
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| 520 | |a © Author(s) (or their employer(s)) 2024. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ. | ||
| 520 | |a 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 | ||
| 520 | |a 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 | ||
| 520 | |a 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 | ||
| 520 | |a 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 | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Autoimmune Diseases | |
| 650 | 4 | |a Autoimmunity | |
| 650 | 4 | |a Lupus Erythematosus, Systemic | |
| 650 | 7 | |a Antibodies, Antinuclear |2 NLM | |
| 700 | 1 | |a Wei, Wei |e verfasserin |4 aut | |
| 700 | 1 | |a Ouyang, Renren |e verfasserin |4 aut | |
| 700 | 1 | |a Chen, Rujia |e verfasserin |4 aut | |
| 700 | 1 | |a Wang, Ting |e verfasserin |4 aut | |
| 700 | 1 | |a Yuan, Xu |e verfasserin |4 aut | |
| 700 | 1 | |a Wang, Feng |e verfasserin |4 aut | |
| 700 | 1 | |a Hou, Hongyan |e verfasserin |4 aut | |
| 700 | 1 | |a Wu, Shiji |e verfasserin |4 aut | |
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