Using Key Predictors in an SVM Model for Differentiating Spinal Fractures and Herniated Intervertebral Discs in Preoperative Anesthesia Evaluation
<b>Background/Objectives:</b> Spinal conditions, such as fractures and herniated intervertebral discs (HIVDs), are often challenging to diagnose due to overlapping clinical symptoms and the difficulty in assessing their functional impact. Accurate differentiation between these conditions...
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2024-11-01
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| author | Shih-Ying Yang Shih-Yen Hsu Yi-Kai Su Nan-Han Lu Kuo-Ying Liu Tai-Been Chen Kon-Ning Chiu Yung-Hui Huang Li-Ren Yeh |
| author_facet | Shih-Ying Yang Shih-Yen Hsu Yi-Kai Su Nan-Han Lu Kuo-Ying Liu Tai-Been Chen Kon-Ning Chiu Yung-Hui Huang Li-Ren Yeh |
| author_sort | Shih-Ying Yang |
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| description | <b>Background/Objectives:</b> Spinal conditions, such as fractures and herniated intervertebral discs (HIVDs), are often challenging to diagnose due to overlapping clinical symptoms and the difficulty in assessing their functional impact. Accurate differentiation between these conditions is crucial for effective treatment, particularly in the context of preoperative anesthesia evaluation, where understanding the underlying condition can influence anesthesia planning and pain management. <b>Methods and Materials:</b> This study presents a Support Vector Machine (SVM) model designed to distinguish between spinal fractures and HIVDs using key clinical predictors, including age, gender, preoperative Visual Analog Scale (VAS) pain scores, and the number of spinal fractures. A retrospective analysis was conducted on a dataset of 199 patients diagnosed with these conditions. The SVM model, using a radial basis function (RBF) kernel, classified the conditions based on the selected predictors. Model performance was evaluated using precision, recall, accuracy, and the Kappa index, with Leave-One-Out (LOO) cross-validation applied to ensure robust results. <b>Results:</b> The SVM model achieved a precision of 92.1% for fracture cases and 91.2% for HIVDs, with recall rates of 98.1% for fractures and 70.5% for HIVDs. The overall accuracy was 92%, and the Kappa index was 0.76, indicating substantial agreement. The analysis revealed that age and VAS pain scores were the most critical predictors for accurately diagnosing these conditions. <b>Conclusions:</b> These results highlight the potential of the SVM model with an RBF kernel to reliably differentiate between spinal fractures and HIVDs using routine clinical data. Future work could enhance model performance by incorporating additional clinical parameters relevant to preoperative anesthesia evaluation. |
| format | Article |
| id | doaj-art-9d4130163d7a4ef387dc92b77ec632f6 |
| institution | Kabale University |
| issn | 2075-4418 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Diagnostics |
| spelling | doaj-art-9d4130163d7a4ef387dc92b77ec632f62024-11-08T14:35:00ZengMDPI AGDiagnostics2075-44182024-11-011421245610.3390/diagnostics14212456Using Key Predictors in an SVM Model for Differentiating Spinal Fractures and Herniated Intervertebral Discs in Preoperative Anesthesia EvaluationShih-Ying Yang0Shih-Yen Hsu1Yi-Kai Su2Nan-Han Lu3Kuo-Ying Liu4Tai-Been Chen5Kon-Ning Chiu6Yung-Hui Huang7Li-Ren Yeh8Department of Anesthesiology, Taoyuan Armed Forces General Hospital, Taoyuan City 30054, TaiwanDepartment of Information Engineering, I-Shou University, Kaohsiung City 82445, TaiwanDepartment of Anesthesiology, E-DA Hospital, I-Shou University, Kaohsiung City 82445, TaiwanDepartment of Radiology, E-DA Cancer Hospital, I-Shou University, Kaohsiung City 82445, TaiwanDepartment of Radiology, E-DA Cancer Hospital, I-Shou University, Kaohsiung City 82445, TaiwanDepartment of Radiological Technology, Faculty of Medical Technology, Teikyo University, Tokyo 173-8605, JapanDepartment of Business Management, National Sun Yat-sen University, Kaohsiung City 82445, TaiwanDepartment of Medical Imaging and Radiological Science, I-Shou University, Kaohsiung City 82445, TaiwanDepartment of Anesthesiology, E-DA Cancer Hospital, I-Shou University, Kaohsiung City 82445, Taiwan<b>Background/Objectives:</b> Spinal conditions, such as fractures and herniated intervertebral discs (HIVDs), are often challenging to diagnose due to overlapping clinical symptoms and the difficulty in assessing their functional impact. Accurate differentiation between these conditions is crucial for effective treatment, particularly in the context of preoperative anesthesia evaluation, where understanding the underlying condition can influence anesthesia planning and pain management. <b>Methods and Materials:</b> This study presents a Support Vector Machine (SVM) model designed to distinguish between spinal fractures and HIVDs using key clinical predictors, including age, gender, preoperative Visual Analog Scale (VAS) pain scores, and the number of spinal fractures. A retrospective analysis was conducted on a dataset of 199 patients diagnosed with these conditions. The SVM model, using a radial basis function (RBF) kernel, classified the conditions based on the selected predictors. Model performance was evaluated using precision, recall, accuracy, and the Kappa index, with Leave-One-Out (LOO) cross-validation applied to ensure robust results. <b>Results:</b> The SVM model achieved a precision of 92.1% for fracture cases and 91.2% for HIVDs, with recall rates of 98.1% for fractures and 70.5% for HIVDs. The overall accuracy was 92%, and the Kappa index was 0.76, indicating substantial agreement. The analysis revealed that age and VAS pain scores were the most critical predictors for accurately diagnosing these conditions. <b>Conclusions:</b> These results highlight the potential of the SVM model with an RBF kernel to reliably differentiate between spinal fractures and HIVDs using routine clinical data. Future work could enhance model performance by incorporating additional clinical parameters relevant to preoperative anesthesia evaluation.https://www.mdpi.com/2075-4418/14/21/2456support vector machinespinal fractureherniated discclassificationpreoperative anesthesia evaluation |
| spellingShingle | Shih-Ying Yang Shih-Yen Hsu Yi-Kai Su Nan-Han Lu Kuo-Ying Liu Tai-Been Chen Kon-Ning Chiu Yung-Hui Huang Li-Ren Yeh Using Key Predictors in an SVM Model for Differentiating Spinal Fractures and Herniated Intervertebral Discs in Preoperative Anesthesia Evaluation Diagnostics support vector machine spinal fracture herniated disc classification preoperative anesthesia evaluation |
| title | Using Key Predictors in an SVM Model for Differentiating Spinal Fractures and Herniated Intervertebral Discs in Preoperative Anesthesia Evaluation |
| title_full | Using Key Predictors in an SVM Model for Differentiating Spinal Fractures and Herniated Intervertebral Discs in Preoperative Anesthesia Evaluation |
| title_fullStr | Using Key Predictors in an SVM Model for Differentiating Spinal Fractures and Herniated Intervertebral Discs in Preoperative Anesthesia Evaluation |
| title_full_unstemmed | Using Key Predictors in an SVM Model for Differentiating Spinal Fractures and Herniated Intervertebral Discs in Preoperative Anesthesia Evaluation |
| title_short | Using Key Predictors in an SVM Model for Differentiating Spinal Fractures and Herniated Intervertebral Discs in Preoperative Anesthesia Evaluation |
| title_sort | using key predictors in an svm model for differentiating spinal fractures and herniated intervertebral discs in preoperative anesthesia evaluation |
| topic | support vector machine spinal fracture herniated disc classification preoperative anesthesia evaluation |
| url | https://www.mdpi.com/2075-4418/14/21/2456 |
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