A health data analysis guide to investigating key factors leading to stroke with Graphext. This guide, written by María López and Paul Suddon, uses Graphext prediction models to dig deeper into a healthcare dataset, selecting the factors most likely to lead people to have a stroke.

Read the full guide here – https://www.graphext.com/docs/predicting-stroke-probability
Watch more guides here – https://www.youtube.com/playlist?listPLTCz–FKgXa-TIyBRGxd61x4G0Dhdf7GI

Healthcare professionals can use advanced data analytics to improve diagnosis, analyze clinical trials, and improve patient care. Our team builds a prediction model to analyze trends among stroke patients.

Finding that a person's age and blood sugar levels are strongly linked to the likelihood of having a stroke, the team is studying the defining characteristics of groups with high numbers of stroke victims.

Along the way, they also debunked a notable trend indicating that marriage is a key factor leading to strokes by explaining the relevance of Simpson's paradox and confusing the variables with the project.

This guide is ideal for people looking to deepen their knowledge of predictive analytics in the healthcare sector.

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