arXiv:2501.00048v2 Announce Type: replace-cross Abstract: Every year in the United States, 800,000 individuals suffer a stroke - one person every 40 seconds, with a death occurring every four minutes. While individual factors vary, certain predictors are more prevalent in determining stroke risk. As strokes are the second leading cause of death and disability worldwide, predicting stroke likelihood based on lifestyle factors is crucial. Showing individuals their stroke risk could motivate lifestyle changes, and machine learning offers solutions to this prediction challenge. Neural networks exc
The increasing availability of large clinical datasets and advancements in machine learning techniques, particularly neural networks, are making robust predictive models for health outcomes more feasible now than ever before.
Predicting stroke risk with higher accuracy can enable proactive interventions, reduce healthcare burden, and potentially save lives, impacting public health and healthcare economics significantly.
The ability to integrate clinical and social features into machine learning models provides a more holistic and potentially more accurate prediction of stroke risk than traditional methods.
- · Healthcare providers
- · Patients at risk of stroke
- · AI/ML healthcare solution providers
- · Public health organizations
- · Traditional diagnostic methods
- · Insurance companies (potentially higher pro-active costs)
- · Individuals ignoring lifestyle changes
More individuals receive early warnings and personalized risk assessments for stroke.
Increased adoption of preventive care strategies and lifestyle modifications in high-risk populations, supported by data-driven insights.
Long-term reduction in stroke incidence and associated healthcare costs, shifting focus towards predictive and preventive medicine.
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Read at arXiv cs.AI