NORMA eResearch @NCI Library

A Predictive Model for Predicting Blood Pressure Levels Using Machine Learning Techniques

Obafemi, Akinwale Sunday (2022) A Predictive Model for Predicting Blood Pressure Levels Using Machine Learning Techniques. Masters thesis, Dublin, National College of Ireland.

[thumbnail of Master of Science]
PDF (Master of Science)
Download (1MB) | Preview
[thumbnail of Configuration manual]
PDF (Configuration manual)
Download (765kB) | Preview


This study examined an efficient model for the accurate prediction of blood pressure levels by building on existing models that are useful for HBP prediction. The best model was selected based on RMSE and MAE evaluation metrics that were used to evaluate their performance. The RMSE metric was used to provide information by doing a term-by-term comparison and showing the value-performance relationship between selected models; while the MAE metric shows the average of absolute errors that the selected models are liable. The data for this study was sourced from because it is publicly available and reduces the chance for ethical misconduct. Features of the data selected are useful for training, testing, and estimation of the study outcome. This was done through the processes of pre-cleaning, visualisation, transformation, engineering, and modelling. The Cat-Boost model outperformed other models with an RMSE score of 7.307459 and an MAE score of 5.790854; this was further confirmed after evaluation by the Grid search presented good evaluation scores where the RMSE and MAE scores were significantly reduced to 0.87773 and 0.12256 respectively. The study also shows that the most significant variable for blood pressure measurement is age while the least significant is the number of major vessels coloured by fluoroscopy (ca 4).

Item Type: Thesis (Masters)
Subjects: Q Science > QA Mathematics > Electronic computers. Computer science
T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science
T Technology > Biomedical engineering
Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Machine learning
H Social Sciences > HM Sociology > Information Science > Communication > Medical Informatics
Divisions: School of Computing > Master of Science in Data Analytics
Depositing User: Tamara Malone
Date Deposited: 23 Feb 2023 17:56
Last Modified: 02 Mar 2023 08:30

Actions (login required)

View Item View Item