What the fuck good does this prediction do? You need to do useful research; like creating EXACT PROTOCOLS that deliver gait independence. Are your mentors and senior researchers that blitheringly stupid?
Send me hate mail on this:
oc1dean@gmail.com. I'll print your complete statement with your name and my
response in my blog. Or are you afraid to engage with my stroke-addled
mind? Your patients need an explanation of why you aren't working on 100% recovery protocols, not this useless prediction crapola!
Logistic regression analysis and machine learning for predicting post-stroke gait independence: a retrospective study
Scientific Reports volume 14, Article number: 21273 (2024)
Abstract
This study investigated whether machine learning (ML) has better predictive accuracy than logistic regression analysis (LR) for gait independence at discharge in subacute stroke patients (n = 843) who could not walk independently at admission. We developed prediction models using LR and five ML algorithms—specifically, the decision tree (DT), support vector machine, artificial neural network, ensemble learning, and k-nearest neighbor methods. Functional Independence Measure sub-items were used to evaluate the ability to walk independently. Model predictive accuracies were evaluated using areas under receiver operating characteristic curves (AUCs) as well as accuracy, precision, recall, F1 score, and specificity. The AUC for DT (0.812) was significantly lower than those for the other algorithms (p < 0.01); however, the AUC for LR (0.895) did not differ significantly from those for the other models (0.893–0.903). Other performance metrics showed no substantial differences between LR and ML algorithms. In conclusion, the DT algorithm had significantly low predictive accuracy, and LR showed no significant difference in predictive accuracy compared with the other ML algorithms. As its predictive accuracy is similar to that of ML, LR can continue to be used for predicting the prognosis of gait independence, with additional advantages of being easily understandable and manually computable.
