Performance analysis of Brain.js machine learning in predicting ischaemic stroke incident: A study in Indonesian tertiary hospital

Key Points

• Analysis shows that machine learning improves the prediction of ischemic stroke incidents, indicating a new approach for early detection.
• The study used Brain.js algorithm, achieving a significant predictive accuracy rate validated in a clinical dataset.
• Assessment demonstrated the viability of predictive modeling in a hospital environment, with a focus on potentially enhancing patient monitoring.
• This approach may enable better health outcomes; further evaluations across different clinical settings are needed.