Hypertension Detection Using Passive-Aggressive Algorithm With The PA-I And PA-II Methods

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M. Hafidz Ariansyah
Sri Winarno


Hypertension is a primary factor in diseases such as stroke, heart failure, myocardial infarction, atrial fibrillation, peripheral arterial disease, and aortic dissection. Early detection of hypertension from medical history is very urgent for the first treatment of patients so that the patient's life expectancy increases, increases the effectiveness of treatment, reduces treatment costs, and reduces the severity of hypertension. Researchers get detection results using a branch of AI technology, namely machine learning to find new knowledge from data and find patterns to make diagnoses. Researchers use machine learning that can explore large amounts of data sets to produce knowledge that is beneficial to science. In this paper, the researchers used the Passive-Aggressive algorithm and the PA-I and PA-II methods to make a model for the diagnosis of hypertension. This algorithm can work well for learning by transforming data and dealing with unbalanced classification problems. PA-I shows stable accuracy of test data with a value of 80.3 - 84.15%, and PA-II shows accuracy instability with a value of 71.41 - 82.41%. From these results, PA-I shows that the model is good in diagnosing hypertension patients because its accuracy is stable and high enough. The results also show that the model is not overfitting, and the new data can be predicted well in line with the training data because, on the results of training accuracy, PA-I shows an accuracy of 81.6 - 84.56% while PA-II shows an accuracy of 71.6 - 82.71%.

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Hypertension Detection Using Passive-Aggressive Algorithm With The PA-I And PA-II Methods. (2023). International Journal of Management and Data Analytics, 3(1), 1-6. https://doi.org/10.5281/zenodo.11527570
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How to Cite

Hypertension Detection Using Passive-Aggressive Algorithm With The PA-I And PA-II Methods. (2023). International Journal of Management and Data Analytics, 3(1), 1-6. https://doi.org/10.5281/zenodo.11527570


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