Hypertension Detection via Tree-Based Stack Ensemble with SMOTE-Tomek Data Balance and XGBoost Meta-Learner DOI Creative Commons
Christopher Chukwufunaya Odiakaose,

Fidelis Obukohwo Aghware,

Margaret Dumebi Okpor

и другие.

Journal of Future Artificial Intelligence and Technologies, Год журнала: 2024, Номер 1(3), С. 269 - 283

Опубликована: Дек. 1, 2024

High blood pressure (or hypertension) is a causative disorder to plethora of other ailments – as it succinctly masks ailments, making them difficult diagnose and manage with targeted treatment plan effectively. While some patients living elevated high can effectively their condition via adjusted lifestyle monitoring follow-up treatments, Others in self-denial leads unreported instances, mishandled cases, now rampant cases result death. Even the usage machine learning schemes medicine, two (2) significant issues abound, namely: (a) utilization dataset construction model, which often yields non-perfect scores, (b) exploration complex deep models have yielded improved accuracy, requires large dataset. To curb these issues, our study explores tree-based stacking ensemble Decision tree, Adaptive Boosting, Random Forest (base learners) while we explore XGBoost meta-learner. With Kaggle retrieved, prediction accuracy 1.00 an F1-score that correctly classified all instances test

Язык: Английский

Hypertension Detection via Tree-Based Stack Ensemble with SMOTE-Tomek Data Balance and XGBoost Meta-Learner DOI Creative Commons
Christopher Chukwufunaya Odiakaose,

Fidelis Obukohwo Aghware,

Margaret Dumebi Okpor

и другие.

Journal of Future Artificial Intelligence and Technologies, Год журнала: 2024, Номер 1(3), С. 269 - 283

Опубликована: Дек. 1, 2024

High blood pressure (or hypertension) is a causative disorder to plethora of other ailments – as it succinctly masks ailments, making them difficult diagnose and manage with targeted treatment plan effectively. While some patients living elevated high can effectively their condition via adjusted lifestyle monitoring follow-up treatments, Others in self-denial leads unreported instances, mishandled cases, now rampant cases result death. Even the usage machine learning schemes medicine, two (2) significant issues abound, namely: (a) utilization dataset construction model, which often yields non-perfect scores, (b) exploration complex deep models have yielded improved accuracy, requires large dataset. To curb these issues, our study explores tree-based stacking ensemble Decision tree, Adaptive Boosting, Random Forest (base learners) while we explore XGBoost meta-learner. With Kaggle retrieved, prediction accuracy 1.00 an F1-score that correctly classified all instances test

Язык: Английский

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