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Hypertension Detection via Tree-Based Stack Ensemble with SMOTE-Tomek Data Balance and XGBoost Meta-Learner

Odiakaose, Christopher Chukwufunaya and Aghware, Fidelis Obukohwo and Okpor, Margaret Dumebi and Eboka, Andrew Okonji and Binitie, Amaka Patience and Ojugo, Arnold Adimabua and Setiadi, De Rosal Ignatius Moses and Ibor, Ayei Egu and Ako, Rita Erhovwo and Geteloma, Victor Ochuko and Ugbotu, Eferhire Valentine and Aghaunor, Tabitha Chukwudi (2024) Hypertension Detection via Tree-Based Stack Ensemble with SMOTE-Tomek Data Balance and XGBoost Meta-Learner. Journal of Future Artificial Intelligence and Technologies, 1 (3). pp. 269-283. ISSN 3048-3719

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Abstract

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.

Item Type: Article
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Depositing User: dl fts
Date Deposited: 01 Dec 2024 13:27
Last Modified: 01 Dec 2024 13:27
URI: https://dl.futuretechsci.org/id/eprint/93

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