Abstract
In this paper, we proposed four different machine learning classification models, i.e., Logistic Regression, Decision Tree, Multilayer Perceptron, and XGBoost, to predict Blood Pressure levels. Moreover, various performance metrics for each model have been calculated, such as accuracy, specificity, precision, recall, and F1 score. According to the findings and comparison of each classification model, XG-Boost achieves the highest classification accuracy of 90%. In contrast, Multilayer Perceptron, Decision Tree, and Logistic Regression achieved 87.33%, 83.83%, and 73.50% for blood pressure classifications, respectively. This study can forecast blood pressure-related diseases in the medical field.
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