Hypertension Risk Prediction using Machine Learning Models and Ensemble Techniques

Khandare, Sarang Sanjay (2024) Hypertension Risk Prediction using Machine Learning Models and Ensemble Techniques. Masters thesis, Dublin, National College of Ireland.

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Abstract

Hypertension has generally been considered the "silent killer" and one of the main health issues in the world because it results in serious complications such as cardiovascular diseases and stroke. The present study investigates the ability of machine learning models to predict hypertension effectively by using combined lifestyle and physiological factors. Thus, this paper uses a dataset based on features comprising age, BMI, cholesterol, glucose, blood pressure, and Smoking status, with preprocessing and certain robust machine learning methodologies in order to derive these predictions. Up to nine different algorithms were compared for their F1-scores, recall, and AUC-ROC-comprised Random Forests, Gradient Boost, CatBoost, and XGBoost. The ensemble method, which combined the strengths of the top performing models, had a very strong predictive power with a F1-score of 0.8528 and recall of 0.8933. Feature importance analysis showed that systolic blood pressure, diastolic blood pressure, age, and BMI were the most influential factors contributing to hypertension risk. The results confirm the effectiveness of machine learning in the detection of hypertension risk. These findings represent a scalable, interpretable solution to improve clinical decision-making. This research therefore calls for an immediate need for data-driven approaches in healthcare to improve patient outcomes.

Item Type:	Thesis (Masters)
Supervisors:	Name Email Nagahmulla, Harshani UNSPECIFIED
Uncontrolled Keywords:	Hypertension; Evaluation metrics; Ensemble models; Gradient Boosting; XGBoost; Random Forest; Feature Importance
Subjects:	Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science R Medicine > Healthcare Industry Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Machine learning
Divisions:	School of Computing > Master of Science in Data Analytics
Depositing User:	Ciara O'Brien
Date Deposited:	03 Sep 2025 10:49
Last Modified:	03 Sep 2025 10:49
URI:	https://norma.ncirl.ie/id/eprint/8729

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