首页|Affiliated Hospital of Hubei University of Arts and Science Reports Findings in Myelopathy (Machine-learning-based prediction by stacking ensemble strategy for surgical outcomes in patients with degenerative cervical myelopathy)
Affiliated Hospital of Hubei University of Arts and Science Reports Findings in Myelopathy (Machine-learning-based prediction by stacking ensemble strategy for surgical outcomes in patients with degenerative cervical myelopathy)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Spinal Cord Diseases a nd Conditions - Myelopathy is the subject of a report. According to news origina ting from Hubei, People’s Republic of China, by NewsRx correspondents, research stated, “Machine learning (ML) is extensively employed for forecasting the outco me of various illnesses. The objective of the study was to develop ML based clas sifiers using a stacking ensemble strategy to predict the Japanese Orthopedic As sociation (JOA) recovery rate for patients with degenerative cervical myelopathy (DCM).” Our news journalists obtained a quote from the research from the Affiliated Hosp ital of Hubei University of Arts and Science, “A total of 672 patients with DCM were included in the study and labeled with JOA recovery rate by 1-year follow-u p. All data were collected during 2012-2023 and were randomly divided into train ing and testing (8:2) sub-datasets. A total of 91 initial ML classifiers were de veloped, and the top 3 initial classifiers with the best performance were furthe r stacked into an ensemble classifier with a supported vector machine (SVM) clas sifier. The area under the curve (AUC) was the main indicator to assess the pred iction performance of all classifiers. The primary predicted outcome was the JOA recovery rate. By applying an ensemble learning strategy (e.g., stacking), the accuracy of the ML classifier improved following combining three widely used ML models (e.g., RFE-SVM, EmbeddingLR-LR, and RFEAdaBoost). Decision curve analysi s showed the merits of the ensemble classifiers, as the curves of the top 3 init ial classifiers varied a lot in predicting JOA recovery rate in DCM patients.”
HubeiPeople’s Republic of ChinaAsiaCyborgsEmerging TechnologiesHealth and MedicineMachine LearningMyelopat hySpinal Cord Diseases and Conditions
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