ArticleOpen medicine (Warsaw, Poland)2019
Comparative Study of Back Propagation Artificial Neural Networks and Logistic Regression Model in Predicting Poor Prognosis after Acute Ischemic Stroke.
Article in Open medicine (Warsaw, Poland), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 2 of them syntheses that pooled it.
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Who cites it
11 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Machine learning prediction of motor function in chronic stroke patients: a systematic review and meta-analysis.Frontiers in neurology · 2023Pooled it
- A systematic review of machine learning models for predicting outcomes of stroke with structured data.PloS one · 2020Pooled it
- Development and validation of a disease-syndrome integrated assessment model for AECOPD severity: protocol for a nationwide multicentre cross-sectional study.BMJ open respiratory research · 2026Article
- Predicting post-stroke functional outcome using explainable machine learning and integrated data.Scientific reports · 2026Article
- Evaluating machine learning models for stroke prediction based on clinical variables.Frontiers in neurology · 2025Article
- Explainable and Interpretable Model for the Early Detection of Brain Stroke Using Optimized Boosting Algorithms.Diagnostics (Basel, Switzerland) · 2024Article
- Prediction of poststroke independent walking using machine learning: a retrospective study.BMC neurology · 2024Article
- Developing machine learning models to predict multi-class functional outcomes and death three months after stroke in Sweden.PloS one · 2024Article
- Prediction of Long-Term Poor Clinical Outcomes in Cerebral Venous Thrombosis Using Neural Networks Model: The BEAST Study.International journal of general medicine · 2024Article
- Optimizing acute stroke outcome prediction models: Comparison of generalized regression neural networks and logistic regressions.PloS one · 2022Article
- Different Data Mining Approaches Based Medical Text Data.Journal of healthcare engineering · 2021Review
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5 authors.
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Abstract
objectiveTo investigate the predictive value of clinical variables on the poor prognosis at 90-day follow-up from acute stroke onset, and compare the diagnostic performance between back propagation artificial neural networks (BP ANNs) and Logistic regression (LR) models in predicting the prognosis.
methodsWe studied the association between clinical variables and the functional recovery of 435 acute ischemic stroke patients. The patients were divided into 2 groups according to modified Rankin Scale scores evaluated on the 90th day after stroke onset. Both BP ANNs and LR models were established for predicting the poor outcome and their diagnostic performance were compared by receiver operating curve.
resultsAge, free fatty acid, homocysteine and alkaline phosphatase were closely related with the poor outcome in acute ischemic stroke patients and finally enrolled in models. The accuracy, sensitivity and specificity of BP ANNs were 80.15%, 75.64% and 82.07% respectively. For the LR model, the accuracy, sensitivity and specificity was 70.61%, 88.46% and 63.04% respectively. The area under the ROC curve of the BP ANNs and LR model was 0.881and 0.809.
conclusionsBoth BP ANNs and LR model were promising for the prediction of poor outcome by combining age, free fatty acid, homocysteine and alkaline phosphatase. However, BP ANNs model showed better performance than LR model in predicting the prognosis.
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