Machine Learning Analysis to Identify Predictive Factors of Caudal Epidural Pulse Radiofrequency in the Treatment of Coccygodynia
Ender Sir,1 Sena Aydogan,2 Gul Didem Batur Sir,2 Alp Eren Celenlioglu1 1Department of Algology and Pain Medicine, University of Health Sciences Gulhane School of Medicine, Ankara, Turkey; 2Department of Industrial Engineering, Gazi University, Ankara, TurkeyCorrespondence: Ender Sir, Email endersir@...
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Dove Medical Press
2025-06-01
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| Series: | Journal of Pain Research |
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| author | Sir E Aydogan S Batur Sir GD Celenlioglu AE |
| author_facet | Sir E Aydogan S Batur Sir GD Celenlioglu AE |
| author_sort | Sir E |
| collection | DOAJ |
| description | Ender Sir,1 Sena Aydogan,2 Gul Didem Batur Sir,2 Alp Eren Celenlioglu1 1Department of Algology and Pain Medicine, University of Health Sciences Gulhane School of Medicine, Ankara, Turkey; 2Department of Industrial Engineering, Gazi University, Ankara, TurkeyCorrespondence: Ender Sir, Email endersir@gmail.comBackground: This study aims to use machine learning (ML) to explore predictive parameters related to the efficacy of caudal epidural pulsed radiofrequency (CEPRF) treatment for coccygodynia.Methods: Five different ML methods were used to predict treatment success at 6 months after CEPRF. The findings generated by these algorithms are compared with respect to the accuracy of the results.Results: Symptom duration, angular deformation and NRS at admission are the most significant factors impacting therapy success in coccygodynia patients. Success rates are obtained for relatively short symptom durations to be 71.83%, for longer periods to be 16.67%; for short durations together with no angular deformity to be 79.55%, with angular deformity to be 59.26%; and for NRS level at admission less than 8 together with angular deformity to be 91.67%, with no angular deformity to be 33.33%.Conclusion: This research reveals the potential of ML methods to improve treatment outcome prediction in coccygodynia. When a new patient is admitted, the ML-generated decision trees provide a quick and precise assessment of the possible success rate of CEPRF treatment.Keywords: machine learning, decision tree, pulsed radiofrequency treatment, chronic pain, pain management |
| format | Article |
| id | doaj-art-ae28a90d977d48fa85d9edc2dbcb5cde |
| institution | Kabale University |
| issn | 1178-7090 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Dove Medical Press |
| record_format | Article |
| series | Journal of Pain Research |
| spelling | doaj-art-ae28a90d977d48fa85d9edc2dbcb5cde2025-08-20T03:26:57ZengDove Medical PressJournal of Pain Research1178-70902025-06-01Volume 18Issue 128392848103676Machine Learning Analysis to Identify Predictive Factors of Caudal Epidural Pulse Radiofrequency in the Treatment of CoccygodyniaSir E0Aydogan S1Batur Sir GD2Celenlioglu AE3Department of Algology and Pain MedicineDepartment of Industrial EngineeringIndustrial EngineeringPain medicine - AlgologyEnder Sir,1 Sena Aydogan,2 Gul Didem Batur Sir,2 Alp Eren Celenlioglu1 1Department of Algology and Pain Medicine, University of Health Sciences Gulhane School of Medicine, Ankara, Turkey; 2Department of Industrial Engineering, Gazi University, Ankara, TurkeyCorrespondence: Ender Sir, Email endersir@gmail.comBackground: This study aims to use machine learning (ML) to explore predictive parameters related to the efficacy of caudal epidural pulsed radiofrequency (CEPRF) treatment for coccygodynia.Methods: Five different ML methods were used to predict treatment success at 6 months after CEPRF. The findings generated by these algorithms are compared with respect to the accuracy of the results.Results: Symptom duration, angular deformation and NRS at admission are the most significant factors impacting therapy success in coccygodynia patients. Success rates are obtained for relatively short symptom durations to be 71.83%, for longer periods to be 16.67%; for short durations together with no angular deformity to be 79.55%, with angular deformity to be 59.26%; and for NRS level at admission less than 8 together with angular deformity to be 91.67%, with no angular deformity to be 33.33%.Conclusion: This research reveals the potential of ML methods to improve treatment outcome prediction in coccygodynia. When a new patient is admitted, the ML-generated decision trees provide a quick and precise assessment of the possible success rate of CEPRF treatment.Keywords: machine learning, decision tree, pulsed radiofrequency treatment, chronic pain, pain managementhttps://www.dovepress.com/machine-learning-analysis-to-identify-predictive-factors-of-caudal-epi-peer-reviewed-fulltext-article-JPRMachine learningDecision treePulsed radiofrequency treatmentChronic painPain management. |
| spellingShingle | Sir E Aydogan S Batur Sir GD Celenlioglu AE Machine Learning Analysis to Identify Predictive Factors of Caudal Epidural Pulse Radiofrequency in the Treatment of Coccygodynia Journal of Pain Research Machine learning Decision tree Pulsed radiofrequency treatment Chronic pain Pain management. |
| title | Machine Learning Analysis to Identify Predictive Factors of Caudal Epidural Pulse Radiofrequency in the Treatment of Coccygodynia |
| title_full | Machine Learning Analysis to Identify Predictive Factors of Caudal Epidural Pulse Radiofrequency in the Treatment of Coccygodynia |
| title_fullStr | Machine Learning Analysis to Identify Predictive Factors of Caudal Epidural Pulse Radiofrequency in the Treatment of Coccygodynia |
| title_full_unstemmed | Machine Learning Analysis to Identify Predictive Factors of Caudal Epidural Pulse Radiofrequency in the Treatment of Coccygodynia |
| title_short | Machine Learning Analysis to Identify Predictive Factors of Caudal Epidural Pulse Radiofrequency in the Treatment of Coccygodynia |
| title_sort | machine learning analysis to identify predictive factors of caudal epidural pulse radiofrequency in the treatment of coccygodynia |
| topic | Machine learning Decision tree Pulsed radiofrequency treatment Chronic pain Pain management. |
| url | https://www.dovepress.com/machine-learning-analysis-to-identify-predictive-factors-of-caudal-epi-peer-reviewed-fulltext-article-JPR |
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