دورية أكاديمية

Development of a predictive model for 1-year postoperative recovery in patients with lumbar disk herniation based on deep learning and machine learning

التفاصيل البيبلوغرافية
العنوان: Development of a predictive model for 1-year postoperative recovery in patients with lumbar disk herniation based on deep learning and machine learning
المؤلفون: Yan Chen, Fabin Lin, Kaifeng Wang, Feng Chen, Ruxian Wang, Minyun Lai, Chunmei Chen, Rui Wang
المصدر: Frontiers in Neurology, Vol 15 (2024)
بيانات النشر: Frontiers Media S.A., 2024.
سنة النشر: 2024
المجموعة: LCC:Neurology. Diseases of the nervous system
مصطلحات موضوعية: predictive model, machine learning, deep learning, lumbar disk herniation, lumbar JOA score, Neurology. Diseases of the nervous system, RC346-429
الوصف: BackgroundThe aim of this study is to develop a predictive model utilizing deep learning and machine learning techniques that will inform clinical decision-making by predicting the 1-year postoperative recovery of patients with lumbar disk herniation.MethodsThe clinical data of 470 inpatients who underwent tubular microdiscectomy (TMD) between January 2018 and January 2021 were retrospectively analyzed as variables. The dataset was randomly divided into a training set (n = 329) and a test set (n = 141) using a 10-fold cross-validation technique. Various deep learning and machine learning algorithms including Random Forests, Extreme Gradient Boosting, Support Vector Machines, Extra Trees, K-Nearest Neighbors, Logistic Regression, Light Gradient Boosting Machine, and MLP (Artificial Neural Networks) were employed to develop predictive models for the recovery of patients with lumbar disk herniation 1 year after surgery. The cure rate score of lumbar JOA score 1 year after TMD was used as an outcome indicator. The primary evaluation metric was the area under the receiver operating characteristic curve (AUC), with additional measures including decision curve analysis (DCA), accuracy, sensitivity, specificity, and others.ResultsThe heat map of the correlation matrix revealed low inter-feature correlation. The predictive model employing both machine learning and deep learning algorithms was constructed using 15 variables after feature engineering. Among the eight algorithms utilized, the MLP algorithm demonstrated the best performance.ConclusionOur study findings demonstrate that the MLP algorithm provides superior predictive performance for the recovery of patients with lumbar disk herniation 1 year after surgery.
نوع الوثيقة: article
وصف الملف: electronic resource
اللغة: English
تدمد: 1664-2295
Relation: https://www.frontiersin.org/articles/10.3389/fneur.2024.1255780/full; https://doaj.org/toc/1664-2295
DOI: 10.3389/fneur.2024.1255780
URL الوصول: https://doaj.org/article/79bd4a8a981848be9cd70d268a2ceaf5
رقم الأكسشن: edsdoj.79bd4a8a981848be9cd70d268a2ceaf5
قاعدة البيانات: Directory of Open Access Journals
الوصف
تدمد:16642295
DOI:10.3389/fneur.2024.1255780