STROKE PREDICTION AND RECOMMENDATION SYSTEM USING MACHINE LEARNING
Abstract
Stroke remains one of the leading causes of death and long-term disability worldwide, with early detection and intervention being critical to improving patient outcomes. This project presents a machine learning-based system designed to predict the likelihood of stroke occurrence using patient health data and to provide personalized recommendations for risk mitigation.
The system analyzes key indicators such as age, hypertension, heart disease, glucose levels, BMI, and lifestyle factors. The model is trained and evaluated using real-world datasets, achieving competitive accuracy and reliability across multiple classifiers.
Beyond prediction, the system integrates a recommendation engine that offers tailored health advice based on individual risk profiles. This dual-functionality approach enhances clinical decision-making and empowers patients with actionable insights to reduce stroke risk.
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