首页|Capital Medical University Reports Findings in Stroke (Prediction of poststroke independent walking using machine learning: a retrospective study)
Capital Medical University Reports Findings in Stroke (Prediction of poststroke independent walking using machine learning: a retrospective study)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Cerebrovascular Diseas es and Conditions - Stroke is the subject of a report. According to news reporti ng out of Beijing, People’s Republic of China, by NewsRx editors, research state d, “Accurately predicting the walking independence of stroke patients is importa nt. Our objective was to determine and compare the performance of logistic regre ssion (LR) and three machine learning models (eXtreme Gradient Boosting (XGBoost ), Support Vector Machines (SVM), and Random Forest (RF)) in predicting walking independence at discharge in stroke patients, as well as to explore the variable s that predict prognosis. 778 (80% for the training set and 20% for the test set) stroke patients admitted to China Rehabilitation Research Cent er between February 2020 and January 2023 were retrospectively included.” Our news journalists obtained a quote from the research from Capital Medical Uni versity, “The training set was used for training models. The test set was used t o validate and compare the performance of the four models in terms of area under the curve (AUC), accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and F1 score. Among the three ML models, the AUC of the XGBoost model is significantly higher than that of the SVM and R F models (P <0.001, P = 0.024, respectively). There was no significant difference in the AUCs between the XGBoost model and the LR model ( 0.891 vs. 0.880, P = 0.560). The XGBoost model demonstrated superior accuracy (8 7.82% vs. 86.54%), sensitivity (50.00% vs. 39.39%), PPV (73.68% vs. 73.33%), NP V (89.78% vs. 87.94%), and F1 score (59.57% vs. 51.16%), with only slightly lower specificity (96.09% vs. 96.88%). Together, the XGBoost model and the stepwise LR model identified age, FMA-LE at admission, FAC at admission, and lower limb spasticity as key factors influencing independent walking. Overall, the XGBoost model perf ormed best in predicting independent walking after stroke.”
BeijingPeople’s Republic of ChinaAsi aCerebrovascular Diseases and ConditionsCyborgsEmerging TechnologiesHeal th and MedicineMachine LearningStroke
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