Deans' stroke musings: Web-based Machine Learning Prediction of Stroke Rehabilitation Exercise Categories

Changing stroke rehab and research worldwide now.Time is Brain! trillions and trillions of neurons that DIE each day because there are NO effective hyperacute therapies besides tPA(only 12% effective). I have 523 posts on hyperacute therapy, enough for researchers to spend decades proving them out. These are my personal ideas and blog on stroke rehabilitation and stroke research. Do not attempt any of these without checking with your medical provider. Unless you join me in agitating, when you need these therapies they won't be there.

What this blog is for:

My blog is not to help survivors recover, it is to have the 10 million yearly stroke survivors light fires underneath their doctors, stroke hospitals and stroke researchers to get stroke solved. 100% recovery. The stroke medical world is completely failing at that goal, they don't even have it as a goal. Shortly after getting out of the hospital and getting NO information on the process or protocols of stroke rehabilitation and recovery I started searching on the internet and found that no other survivor received useful information. This is an attempt to cover all stroke rehabilitation information that should be readily available to survivors so they can talk with informed knowledge to their medical staff. It lays out what needs to be done to get stroke survivors closer to 100% recovery. It's quite disgusting that this information is not available from every stroke association and doctors group.

Monday, April 20, 2026

Web-based Machine Learning Prediction of Stroke Rehabilitation Exercise Categories

This may be what you should be doing, but missing is the EXACT INTERVENTIONS that will actually allow you to do them! Where are the recovery protocols?

Web-based Machine Learning Prediction of Stroke Rehabilitation Exercise Categories

Elly Johana Johan *1,

Nurul Izah Md Salleh 2,

Norizan Mat Diah 2,

Zainura Idrus 2

1 Department of Computer and Mathematical Sciences, Universiti Teknologi MARA Cawangan Pulau Pinang, Permatang Pauh Campus, 13500 Permatang Pauh, Pulau Pinang

2 Faculty of Computer and Mathematical Sciences, Universiti Teknologi MARA, 40450 Shah Alam, Selangor, Malaysia

Corresponding Authors’ Email Address: ellyjohana@uitm.edu.my

ABSTRACT

Stroke rehabilitation requires timely and targeted exercise interventions to restore mobility, strength, and independence. This study develops a machine learning-based system to predict appropriate rehabilitation exercise categories (strength, balance, and mobility) tailored to patient severity levels. Using an open-source dataset of 5,110 stroke patient records, including age, BMI, glucose level, smoking status, paralysis type, and speech ability, three supervised algorithms were evaluated: Random Forest (RF), Logistic Regression (LR), and Multilayer Perceptron (MLP). Accuracy values were reported with 95% Confidence Intervals (CI): RF (94.12%, 95% CI: 93.6–94.6), LR (94.12%, 95% CI: 93.5–94.7), and MLP (94.32%, 95% CI: 93.8–94.9). Despite MLP’s marginally higher accuracy, RF was selected for deployment due to its stability, interpretability, and alignment with expert recommendations. Validation against rehabilitation specialists yielded strong agreement (Cohen’s κ = 0.82), confirming clinical reliability. The RF model was integrated into a web-based application hosted on Heroku. This platform enables patients, particularly those in rural areas with limited access to physiotherapists, to receive personalised exercise guidance. Future work will expand dataset diversity, incorporate hyperparameter optimisation, and evaluate additional metrics such as precision, recall, F1-score, and ROC-AUC to enhance clinical robustness. This system demonstrates the potential of machine learning to support accessible, personalised rehabilitation in resource-constrained settings.