Hypertension Detection via Tree-Based Stack Ensemble with SMOTE-Tomek Data Balance and XGBoost Meta-Learner - Odiakaose, Christopher Chukwufunaya; Aghware, Fidelis Obukohwo; Okpor, Margaret Dumebi; Eboka, Andrew Okonji; Binitie, Amaka Patience; Ojugo, Arnold Adimabua; Setiadi, De Rosal Ignatius Moses; Ibor, Ayei Egu; Ako, Rita Erhovwo; Geteloma, Victor Ochuko; Ugbotu, Eferhire Valentine; Aghaunor, Tabitha Chukwudi (Journal of Future Artificial Intelligence and Technologies, 2024)
High blood pressure (or hypertension) is a causative disorder to a plethora of other ailments – as it succinctly masks other ailments, making them difficult to diagnose and manage with a targeted treatment plan effectively. While some patients living with elevated high blood pressure can effectively manage their condition via adjusted lifestyle and monitoring with follow-up treatments, Others in self-denial leads to unreported instances, mishandled cases, and in now rampant cases – result in death. Even with the usage of machine learning schemes in medicine, two (2) significant issues abound, namely: (a) utilization of dataset in the construction of the model, which often yields non-perfect scores, and (b) the exploration of complex deep learning models have yielded improved accuracy, which often requires large dataset. To curb these issues, our study explores the tree-based stacking ensemble with Decision tree, Adaptive Boosting, and Random Forest (base learners) while we explore the XGBoost as a meta-learner. With the Kaggle dataset as retrieved, our stacking ensemble yields a prediction accuracy of 1.00 and an F1-score of 1.00 that effectively correctly classified all instances of the test dataset.