首页|Tongji Hospital of Tongji Medical College of Huazhong University of Science and Technology Reports Findings in Rheumatic Diseases and Conditions (Novel multiclass classification machine learning approach for the early-stage classification of ...)
Tongji Hospital of Tongji Medical College of Huazhong University of Science and Technology Reports Findings in Rheumatic Diseases and Conditions (Novel multiclass classification machine learning approach for the early-stage classification of ...)
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New research on Musculoskeletal Diseases and Conditions Rheumatic Diseases and Conditions is the subject of a report. According to news reporting out of Hubei, People’s Republic of China, by NewsRx editors, research stated, “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.” Our news journalists obtained a quote from the research from the Tongji Hospital of Tongji Medical College of Huazhong University of Science and Technology, “A total of 925 SARDs patients were included, categorised into SLE, Sjogren’s syndrome (SS) and inflammatory myositis (IM). Clinical characteristics and laboratory markers were collected and nine key indicators, including anti-dsDNA, anti-SS-A60, antiSm/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. 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. This pioneering multiclass ML model, using basic laboratory indicators, enhances clinical feasibility and demonstrates promising potential for SARDs classification.”
HubeiPeople’s Republic of ChinaAsiaAutoimmunityCyborgsEmerging TechnologiesHealth and MedicineImmunologyMachine LearningMusculoskeletal Diseases and ConditionsRheumatic Diseases and ConditionsSkin and Connective Tissue Diseases and Conditions
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