Department of Orthopedics (Spine Surgery), The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, Zhejiang, China.
Health Science Center, Ningbo University, Ningbo, 315211, Zhejiang, China.
Comput Biol Med. 2024 May;173:108297. doi: 10.1016/j.compbiomed.2024.108297. Epub 2024 Mar 14.
Percutaneous endoscopic lumbar discectomy (PELD) is one of the main means of minimally invasive spinal surgery, and is an effective means of treating lumbar disc herniation, but its early recurrence is still difficult to predict. With the development of machine learning technology, the auxiliary model based on the prediction of early recurrent lumbar disc herniation (rLDH) and the identification of causative risk factors have become urgent problems in current research. However, the screening ability of current models for key factors affecting the prediction of rLDH, as well as their predictive ability, needs to be improved. Therefore, this paper presents a classification model that utilizes wrapper feature selection, developed through the integration of an enhanced bat algorithm (BDGBA) and support vector machine (SVM). Among them, BDGBA increases the population diversity and improves the population quality by introducing directional mutation strategy and guidance-based strategy, which in turn allows the model to secure better subsets of features. Furthermore, SVM serves as the classifier for the wrapper feature selection method, with its classification prediction results acting as a fitness function for the feature subset. In the proposed prediction method, BDGBA is used as an optimizer for feature subset filtering and as an objective function for feature subset evaluation based on the classification results of the support vector machine, which improves the interpretability and prediction accuracy of the model. In order to verify the performance of the proposed method, this paper proves the performance of the model through global optimization experiments and prediction experiments on real data sets. The accuracy of the proposed rLDH prediction model is 93.49% and sensitivity is 88.33%. The experimental results show that Level of herniated disk, Modic change, Disk height, Disk length, and Disk width are the key factors for predicting rLDH, and the proposed method is an effective auxiliary diagnosis method.
经皮内镜腰椎间盘切除术（PELD）是微创脊柱外科的主要手段之一，是治疗腰椎间盘突出症的有效手段，但早期复发仍难以预测。随着机器学习技术的发展，基于早期复发性腰椎间盘突出症（rLDH）预测和识别致病危险因素的辅助模型已成为当前研究的迫切问题。然而，当前模型对影响 rLDH 预测的关键因素的筛选能力以及其预测能力仍有待提高。因此，本文提出了一种分类模型，该模型利用包装特征选择，通过集成增强蝙蝠算法（BDGBA）和支持向量机（SVM）来开发。其中，BDGBA 通过引入定向突变策略和基于指导的策略来增加种群多样性并提高种群质量，从而使模型能够获得更好的特征子集。此外，SVM 作为包装特征选择方法的分类器，其分类预测结果作为特征子集的适应度函数。在所提出的预测方法中，BDGBA 用作特征子集过滤的优化器，并用作基于支持向量机分类结果的特征子集评估的目标函数，从而提高了模型的可解释性和预测精度。为了验证所提出方法的性能，本文通过全局优化实验和真实数据集的预测实验证明了模型的性能。所提出的 rLDH 预测模型的准确率为 93.49%，灵敏度为 88.33%。实验结果表明，椎间盘突出程度、Modic 改变、椎间盘高度、椎间盘长度和椎间盘宽度是预测 rLDH 的关键因素，所提出的方法是一种有效的辅助诊断方法。
