Prediction of Blood Pressure Using Machine Learning

Authors

  • Sirsho Das, Subir Kumar Roy, Soumyajit Chakraborty, Prianka Dey, Dwaipayan Ghosh

Keywords:

Support Vector Classifier, Gaussian Naive Bayes Classifier, Random Forest Classifier, Blood pressure, Confusion Matrix, Regression Model, Hypertension, Machine Learning

Abstract

In the field of digital health today, Blood pressure prediction is very much crucial. Including lots of health conditions besides hypertension, this model in effect can provide practitioners with early warning red flags for events that are coming. In this study, we took the lead in establishing a blood pressure forecast model with three advanced Machine Learning methods i.e. Support Vector Classifier, Random Forest Classifier, and Naive Bayes Classifier. Our study serves the purpose to construct a blood pressure level forecasting tool, based on clinical data. As part of our research, we have gone through the process of collecting a data set and converted it to digital form. It includes important clinical markers that are closely related to blood pressure, including gender, age, body mass index, smoking status, body mass index, and diastolic and systolic blood pressure. We used clean and compare selection techniques to increase the predictive accuracy of our models without increasing their complexity beyond a usable level. Every algorithm was therefore carefully trained and tested against our collected data (2500 training data, 500 testing data). These trials allowed us to tease out different data points on what makes success in prediction such as predicting blood pressure. This research shows the potential of combined Machine Learning methods to better predict long-term outcomes resulting from hypertension. These predictive models will greatly help healthcare professionals in their early detection and targeted intervention for high blood pressure problems.

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References

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Published

09.07.2024

How to Cite

Sirsho Das. (2024). Prediction of Blood Pressure Using Machine Learning. International Journal of Intelligent Systems and Applications in Engineering, 12(22s), 579–586. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/6523

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Section

Research Article