MACHINE LEARNING-BASED PREDICTION OF STROKE RISK FACTORS
Keywords:
Stroke, risk factors, machine learningAbstract
This study investigates the application of machine learning techniques for predicting stroke risk factors, a critical aspect in preventive healthcare. Stroke, a leading cause of disability and mortality worldwide, is often associated with various risk factors such as hypertension, diabetes, smoking, and obesity. Traditional risk assessment methods may lack accuracy and scalability, prompting the exploration of machine learning models for more effective prediction. Leveraging large-scale healthcare datasets, including electronic health records and demographic information, we develop and evaluate machine learning algorithms to predict the likelihood of stroke and identify significant risk factors. By harnessing advanced data analytics and predictive modeling techniques, our approach aims to facilitate early detection and intervention strategies, ultimately improving stroke prevention and patient outcomes.
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Copyright (c) 2024 Oliver Danjuma
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