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<p>Early prediction of disc herniation is essential for timely intervention and prevention. This condition, caused by the protrusion of spinal disc material, can lead to pain, numbness, or paralysis. Accurate detection enables conservative treatments, reducing the need for surgery, and supports preventive strategies for at-risk individuals. This study evaluates the performance of Decision Tree Classification (DTC) and CatBoost Classification (CATC) models for predicting disc herniation and spondylolisthesis, incorporating Sand Cat Swarm Optimization (SCSO) and Adaptive Opposition Slime Mould Algorithm (AOSA) to enhance accuracy. Results indicate that the CAAO model (CATC + AOSA) achieved the highest accuracy (95.8%), followed by the CASC model (CATC + SCSO) (94.4%), while the CATC model (CATC without optimization) had the lowest (94.0%). DTC and its variants showed lower accuracy, highlighting the importance of algorithm selection and optimization for improved diagnostic precision.</p>

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Development of advanced predictive mathematical frameworks for the analysis and diagnosis of disc herniation and spondylolisthesis in spinal conditions

  • 期刊论文

  • 发表日期：

    2025年5月9日

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  • Chen Feng,
  • Hongli Wen,
  • Lianjin Jin,
  • Gang Li

摘要

Early prediction of disc herniation is essential for timely intervention and prevention. This condition, caused by the protrusion of spinal disc material, can lead to pain, numbness, or paralysis. Accurate detection enables conservative treatments, reducing the need for surgery, and supports preventive strategies for at-risk individuals. This study evaluates the performance of Decision Tree Classification (DTC) and CatBoost Classification (CATC) models for predicting disc herniation and spondylolisthesis, incorporating Sand Cat Swarm Optimization (SCSO) and Adaptive Opposition Slime Mould Algorithm (AOSA) to enhance accuracy. Results indicate that the CAAO model (CATC + AOSA) achieved the highest accuracy (95.8%), followed by the CASC model (CATC + SCSO) (94.4%), while the CATC model (CATC without optimization) had the lowest (94.0%). DTC and its variants showed lower accuracy, highlighting the importance of algorithm selection and optimization for improved diagnostic precision.