An Analysis of the Influencing Factors Associated with Hypertension

In contemporary society, hypertension, as a prevalent cardiovascular disease, has exhibited a steady increase in prevalence and maintains a persistently high mortality rate among cardiac-related conditions. Currently, this disease poses a significant threat to patients' health and well-being. This study aims to investigate the primary factors influencing the incidence of hypertension through an in-depth analysis of demographic data. The research conducted analytical examinations on the official dataset from the Kaggle platform. Initially, descriptive analysis was performed, utilizing histograms to visually demonstrate the impact of factors such as heart rate, cholesterol levels, and Body Mass Index (BMI) index on hypertension. Subsequently, logistic regression analysis was employed to explore the relationships between hypertension incidence and variables including heart rate, cholesterol, age, BMI, and BPMeds. Based on this analysis, a binary logistic regression model was established, and predictive evaluation was conducted using the constructed model. The research findings indicate that the model's overall prediction accuracy is approximately 75%, providing a valuable framework for hypertension risk assessment in both general and specific populations.