Linyu Geng1, Wenqiang Qu2, Sen Wang3, Jiaqi Chen2, Yang Xu4, Wei Kong1, Xue Xu1, Xuebing Feng1, Cheng Zhao5, Jun Liang6, Huayong Zhang7, Lingyun Sun1. 1. Department of Rheumatology and Immunology, The Affiliated Drum Tower Hospital, Nanjing University Medical School, 321 Zhongshan Road, Nanjing, China. 2. School of Computer and Information, Hohai University, Nanjing, China. 3. Department of Clinical Laboratory Medicine, Nanjing Drum Tower Hospital, Medical School of Nanjing University, Nanjing, China. 4. The 7Th Outpatient Clinic, Jinling Hospital, Nanjing, China. 5. Department of Rheumatology and Immunology, The Affiliated Drum Tower Hospital, Nanjing University Medical School, 321 Zhongshan Road, Nanjing, China. zhaochengjs@sina.com. 6. Department of Rheumatology and Immunology, The Affiliated Drum Tower Hospital, Nanjing University Medical School, 321 Zhongshan Road, Nanjing, China. 13505193169@139.com. 7. Department of Rheumatology and Immunology, The Affiliated Drum Tower Hospital, Nanjing University Medical School, 321 Zhongshan Road, Nanjing, China. huayong.zhang@nju.edu.cn.
Abstract
OBJECTIVES: To analyze and evaluate the effectiveness of the detection of single autoantibody and combined autoantibodies in patients with rheumatoid arthritis (RA) and related autoimmune diseases and establish a machine learning model to predict the disease of RA. METHODS: A total of 309 patients with joint pain as the first symptom were retrieved from the database. The effectiveness of single and combined antibodies tests was analyzed and evaluated in patients with RA, a cost-sensitive neural network (CSNN) model was used to integrate multiple autoantibodies and patient symptoms to predict the diagnosis of RA, and the ROC curve was used to analyze the diagnosis performance and calculate the optimal cutoff value. RESULTS: There are differences in the seropositive rate of autoimmune diseases, the sensitivity and specificity of single or multiple autoantibody tests were insufficient, and anti-CCP performed best in RA diagnosis and had high diagnostic value. The cost-sensitive neural network prediction model had a sensitivity of up to 0.90 and specificity of up to 0.86, which was better than a single antibody and combined multiple antibody detection. CONCLUSION: In-depth analysis of autoantibodies and reliable early diagnosis based on the neural network could guide specialized physicians to develop different treatment plans to prevent deterioration and enable early treatment with antirheumatic drugs for remission. Key Points • There are differences in the seropositive rate of autoimmune diseases. • This is the first study to use a cost-sensitive neural network model to diagnose RA disease in patients. • The diagnosis effect of the cost-sensitive neural network model is better than a single antibody and combined multiple antibody detection.
OBJECTIVES: To analyze and evaluate the effectiveness of the detection of single autoantibody and combined autoantibodies in patients with rheumatoid arthritis (RA) and related autoimmune diseases and establish a machine learning model to predict the disease of RA. METHODS: A total of 309 patients with joint pain as the first symptom were retrieved from the database. The effectiveness of single and combined antibodies tests was analyzed and evaluated in patients with RA, a cost-sensitive neural network (CSNN) model was used to integrate multiple autoantibodies and patient symptoms to predict the diagnosis of RA, and the ROC curve was used to analyze the diagnosis performance and calculate the optimal cutoff value. RESULTS: There are differences in the seropositive rate of autoimmune diseases, the sensitivity and specificity of single or multiple autoantibody tests were insufficient, and anti-CCP performed best in RA diagnosis and had high diagnostic value. The cost-sensitive neural network prediction model had a sensitivity of up to 0.90 and specificity of up to 0.86, which was better than a single antibody and combined multiple antibody detection. CONCLUSION: In-depth analysis of autoantibodies and reliable early diagnosis based on the neural network could guide specialized physicians to develop different treatment plans to prevent deterioration and enable early treatment with antirheumatic drugs for remission. Key Points • There are differences in the seropositive rate of autoimmune diseases. • This is the first study to use a cost-sensitive neural network model to diagnose RA disease in patients. • The diagnosis effect of the cost-sensitive neural network model is better than a single antibody and combined multiple antibody detection.