Using Bayesian Networks For Predicting Hypertension Risk Factors

Hypertension, a primary cause of mortality as well as morbidity in the cardiovascular system, demands accurate prediction models to ensure effective treatment and preventative measures. This study predicts hypertension associated risk factors using Bayesian Systems, an interpretative mindset, a deductive approach, and an exploratory approach. Using secondary data including genetic, lifestyle, as well as environmental characteristics, the study provides a comparison to established statistical methodologies. The Bayesian neural network model performs admirably in terms of prediction, with an accuracy of more than 85%. Both sensitivity and specificity show values above 80% and 90%, respectively, suggesting proficiency in diagnosing hypertensive people. Furthermore, sensitivity analysis shows that the model is resistant to changes in input parameters. Key contributions to hypertension risk are found, with biological factors, lifestyle choices, as well as exposure to environmental stressors all playing a substantial role. These factors' complicated relationships are explored, revealing their combined impact on antihypertensive susceptibility. Bayesian networks were able to beat traditional statistical methods in terms of accuracy and selectivity in predicting the incidence of hypertension variables. This demonstrates how Bayesian techniques have a potential to transform risk assessment as well as prevention tactics in cardiovascular health. Finally, this study demonstrates the effectiveness of Bayesian models in predicting risk for hypertension using multiple variables, giving a solid framework for focused interventions and preventative measures. The findings hold great promise for furthering public health measures aimed at controlling hypertensive and its effects.