High blood pressure is a leading contributor to cardiovascular diseases, stroke, and chronic kidney disease, particularly in low- and middle-income countries (LMICs). As its prevalence continues to rise, innovative solutions are needed for early detection and management. A recent study has developed a machine learning model to predict high blood pressure using data from 57 countries across six WHO regions.
The model identifies key predictors such as age, weight, heart rate, waist circumference, and height, aligning with established clinical knowledge. This explainable AI model offers a practical tool for population-level screening, particularly in resource-limited settings. By prioritizing high-risk individuals, it can improve outcomes and reduce the economic burden of high blood pressure management.
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