Predicting autoimmune thyroiditis in primary Sjogren’s syndrome patients using a random forest classifier: a retrospective study
Abstract Background Primary Sjogren’s syndrome (pSS) and autoimmune thyroiditis (AIT) share overlapping genetic and immunological profiles. This retrospective study evaluates the efficacy of machine learning algorithms, with a focus on the Random Forest Classifier, to predict the presence of thyroid...
Saved in:
| Main Authors: | , , , , , , , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
BMC
2025-01-01
|
| Series: | Arthritis Research & Therapy |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s13075-024-03469-5 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1841559204679122944 |
|---|---|
| author | Jia-yun Wu Jing-yu Zhang Wen-qi Xia Yue-ning Kang Ru-yi Liao Yu-ling Chen Xiao-min Li Ya Wen Fan-xuan Meng Li-ling Xu Sheng-hui Wen Hui-fen Liu Yuan-qing Li Jie-ruo Gu Qing Lv Yong Ren |
| author_facet | Jia-yun Wu Jing-yu Zhang Wen-qi Xia Yue-ning Kang Ru-yi Liao Yu-ling Chen Xiao-min Li Ya Wen Fan-xuan Meng Li-ling Xu Sheng-hui Wen Hui-fen Liu Yuan-qing Li Jie-ruo Gu Qing Lv Yong Ren |
| author_sort | Jia-yun Wu |
| collection | DOAJ |
| description | Abstract Background Primary Sjogren’s syndrome (pSS) and autoimmune thyroiditis (AIT) share overlapping genetic and immunological profiles. This retrospective study evaluates the efficacy of machine learning algorithms, with a focus on the Random Forest Classifier, to predict the presence of thyroid-specific autoantibodies (TPOAb and TgAb) in pSS patients. Methods A total of 96 patients with pSS were included in the retrospective study. All participants underwent a complete clinical and laboratory evaluation. All participants underwent thyroid function tests, including TPOAb and TgAb, and were accordingly divided into positive and negative thyroid autoantibody groups. Four machine learning algorithms were then used to analyze the risk factors affecting patients with pSS with positive and negative for thyroid autoantibodies. Results The results indicated that the Random Forest Classifier algorithm (AUC = 0.755) outperformed the other three machine learning algorithms. The random forest classifier indicated Age, IgG, C4 and dry mouth were the main factors influencing the prediction of positive thyroid autoantibodies in pSS patients. It is feasible to predict AIT in pSS using machine learning algorithms. Conclusions Analyzing clinical and laboratory data from 96 pSS patients, the Random Forest model demonstrated superior performance (AUC = 0.755), identifying age, IgG levels, complement component 4 (C4), and absence of dry mouth as primary predictors. This approach offers a promising tool for early identification and management of AIT in pSS patients. Trial registration This retrospective study was approved and monitored by the Ethics Committee of The Third Affiliated Hospital of Sun Yat-sen University (No.II2023-254-02). |
| format | Article |
| id | doaj-art-4218564404524c3e910b0527338069d0 |
| institution | Kabale University |
| issn | 1478-6362 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | BMC |
| record_format | Article |
| series | Arthritis Research & Therapy |
| spelling | doaj-art-4218564404524c3e910b0527338069d02025-01-05T12:42:21ZengBMCArthritis Research & Therapy1478-63622025-01-012711710.1186/s13075-024-03469-5Predicting autoimmune thyroiditis in primary Sjogren’s syndrome patients using a random forest classifier: a retrospective studyJia-yun Wu0Jing-yu Zhang1Wen-qi Xia2Yue-ning Kang3Ru-yi Liao4Yu-ling Chen5Xiao-min Li6Ya Wen7Fan-xuan Meng8Li-ling Xu9Sheng-hui Wen10Hui-fen Liu11Yuan-qing Li12Jie-ruo Gu13Qing Lv14Yong Ren15Department of Rheumatology, The Seventh Affiliated Hospital of Sun Yat-Sen UniversityDepartment of Rheumatology, The Seventh Affiliated Hospital of Sun Yat-Sen UniversityDepartment of Rheumatology, The Seventh Affiliated Hospital of Sun Yat-Sen UniversityDepartment of Rheumatology, The Seventh Affiliated Hospital of Sun Yat-Sen UniversityDepartment of Rheumatology, The Seventh Affiliated Hospital of Sun Yat-Sen UniversityDepartment of Rheumatology, The Seventh Affiliated Hospital of Sun Yat-Sen UniversityDepartment of Rheumatology, The Seventh Affiliated Hospital of Sun Yat-Sen UniversityDepartment of Rheumatology, The Seventh Affiliated Hospital of Sun Yat-Sen UniversityDepartment of Rheumatology, The Seventh Affiliated Hospital of Sun Yat-Sen UniversityDepartment of Rheumatology, The Seventh Affiliated Hospital of Sun Yat-Sen UniversityDepartment of Rheumatology, The Seventh Affiliated Hospital of Sun Yat-Sen UniversityDepartment of Rheumatology, The Seventh Affiliated Hospital of Sun Yat-Sen UniversitySchool of Automation Science and Engineering, South China University of TechnologyDepartment of Rheumatology, The Seventh Affiliated Hospital of Sun Yat-Sen UniversityDepartment of Rheumatology, The Seventh Affiliated Hospital of Sun Yat-Sen UniversityScientific Research Project Department, Guangdong Artificial Intelligence and Digital Economy Laboratory (Guangzhou), Pazhou LabAbstract Background Primary Sjogren’s syndrome (pSS) and autoimmune thyroiditis (AIT) share overlapping genetic and immunological profiles. This retrospective study evaluates the efficacy of machine learning algorithms, with a focus on the Random Forest Classifier, to predict the presence of thyroid-specific autoantibodies (TPOAb and TgAb) in pSS patients. Methods A total of 96 patients with pSS were included in the retrospective study. All participants underwent a complete clinical and laboratory evaluation. All participants underwent thyroid function tests, including TPOAb and TgAb, and were accordingly divided into positive and negative thyroid autoantibody groups. Four machine learning algorithms were then used to analyze the risk factors affecting patients with pSS with positive and negative for thyroid autoantibodies. Results The results indicated that the Random Forest Classifier algorithm (AUC = 0.755) outperformed the other three machine learning algorithms. The random forest classifier indicated Age, IgG, C4 and dry mouth were the main factors influencing the prediction of positive thyroid autoantibodies in pSS patients. It is feasible to predict AIT in pSS using machine learning algorithms. Conclusions Analyzing clinical and laboratory data from 96 pSS patients, the Random Forest model demonstrated superior performance (AUC = 0.755), identifying age, IgG levels, complement component 4 (C4), and absence of dry mouth as primary predictors. This approach offers a promising tool for early identification and management of AIT in pSS patients. Trial registration This retrospective study was approved and monitored by the Ethics Committee of The Third Affiliated Hospital of Sun Yat-sen University (No.II2023-254-02).https://doi.org/10.1186/s13075-024-03469-5Primary Sjogren’s syndromeAutoimmune thyroiditisPredictorsMachine learning algorithms |
| spellingShingle | Jia-yun Wu Jing-yu Zhang Wen-qi Xia Yue-ning Kang Ru-yi Liao Yu-ling Chen Xiao-min Li Ya Wen Fan-xuan Meng Li-ling Xu Sheng-hui Wen Hui-fen Liu Yuan-qing Li Jie-ruo Gu Qing Lv Yong Ren Predicting autoimmune thyroiditis in primary Sjogren’s syndrome patients using a random forest classifier: a retrospective study Arthritis Research & Therapy Primary Sjogren’s syndrome Autoimmune thyroiditis Predictors Machine learning algorithms |
| title | Predicting autoimmune thyroiditis in primary Sjogren’s syndrome patients using a random forest classifier: a retrospective study |
| title_full | Predicting autoimmune thyroiditis in primary Sjogren’s syndrome patients using a random forest classifier: a retrospective study |
| title_fullStr | Predicting autoimmune thyroiditis in primary Sjogren’s syndrome patients using a random forest classifier: a retrospective study |
| title_full_unstemmed | Predicting autoimmune thyroiditis in primary Sjogren’s syndrome patients using a random forest classifier: a retrospective study |
| title_short | Predicting autoimmune thyroiditis in primary Sjogren’s syndrome patients using a random forest classifier: a retrospective study |
| title_sort | predicting autoimmune thyroiditis in primary sjogren s syndrome patients using a random forest classifier a retrospective study |
| topic | Primary Sjogren’s syndrome Autoimmune thyroiditis Predictors Machine learning algorithms |
| url | https://doi.org/10.1186/s13075-024-03469-5 |
| work_keys_str_mv | AT jiayunwu predictingautoimmunethyroiditisinprimarysjogrenssyndromepatientsusingarandomforestclassifieraretrospectivestudy AT jingyuzhang predictingautoimmunethyroiditisinprimarysjogrenssyndromepatientsusingarandomforestclassifieraretrospectivestudy AT wenqixia predictingautoimmunethyroiditisinprimarysjogrenssyndromepatientsusingarandomforestclassifieraretrospectivestudy AT yueningkang predictingautoimmunethyroiditisinprimarysjogrenssyndromepatientsusingarandomforestclassifieraretrospectivestudy AT ruyiliao predictingautoimmunethyroiditisinprimarysjogrenssyndromepatientsusingarandomforestclassifieraretrospectivestudy AT yulingchen predictingautoimmunethyroiditisinprimarysjogrenssyndromepatientsusingarandomforestclassifieraretrospectivestudy AT xiaominli predictingautoimmunethyroiditisinprimarysjogrenssyndromepatientsusingarandomforestclassifieraretrospectivestudy AT yawen predictingautoimmunethyroiditisinprimarysjogrenssyndromepatientsusingarandomforestclassifieraretrospectivestudy AT fanxuanmeng predictingautoimmunethyroiditisinprimarysjogrenssyndromepatientsusingarandomforestclassifieraretrospectivestudy AT lilingxu predictingautoimmunethyroiditisinprimarysjogrenssyndromepatientsusingarandomforestclassifieraretrospectivestudy AT shenghuiwen predictingautoimmunethyroiditisinprimarysjogrenssyndromepatientsusingarandomforestclassifieraretrospectivestudy AT huifenliu predictingautoimmunethyroiditisinprimarysjogrenssyndromepatientsusingarandomforestclassifieraretrospectivestudy AT yuanqingli predictingautoimmunethyroiditisinprimarysjogrenssyndromepatientsusingarandomforestclassifieraretrospectivestudy AT jieruogu predictingautoimmunethyroiditisinprimarysjogrenssyndromepatientsusingarandomforestclassifieraretrospectivestudy AT qinglv predictingautoimmunethyroiditisinprimarysjogrenssyndromepatientsusingarandomforestclassifieraretrospectivestudy AT yongren predictingautoimmunethyroiditisinprimarysjogrenssyndromepatientsusingarandomforestclassifieraretrospectivestudy |