A Guide to Predicting Stroke Probability | Healthcare Data Analytics

A Healthcare Data Analytics guide to Investigating the Key Factors Leading to Strokes with Graphext. This guide, from María López and Paul Suddon, uses Graphext's prediction models to dig deep into a healthcare dataset, picking out the factors most likely to lead people to have a stroke.

Healthcare professionals are able to use advanced data analytics to enhance diagnosis, analyze clinical trials and improve patient care. Our team build a prediction model in order to analyze patterns between patients that have suffered from a stroke.

Finding that a person's age and blood glucose level are strongly related to the probability of suffering a stroke, the team investigate the defining characteristics of clusters featuring a high number of stroke victims.

Along the way, they also debunk a noticeable trend indicating that marriage is a key factor in leading to strokes by explaining the relevance of Simpson's Paradox and confounding variables to the project.

This guide is ideal for people wanting to expand their knowledge of predictive analytics in the healthcare industry.