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

Citrus diseases detection using innovative deep learning approach and Hybrid Meta-Heuristic DOI Creative Commons
N. M. Butt, Muhammad Munwar Iqbal, Shabana Ramzan

и другие.

PLoS ONE, Год журнала: 2025, Номер 20(1), С. e0316081 - e0316081

Опубликована: Янв. 22, 2025

Citrus farming is one of the major agricultural sectors Pakistan and currently represents almost 30% total fruit production, with its highest concentration in Punjab. Although economically important, citrus crops like sweet orange, grapefruit, lemon, mandarins face various diseases canker, scab, black spot, which lower quality yield. Traditional manual disease diagnosis not only slow, less accurate, expensive but also relies heavily on expert intervention. To address these issues, this research examines implementation an automated classification system using deep learning optimal feature selection. The incorporates data augmentation transfer pre-trained models such as DenseNet-201 AlexNet to improve diagnostic accuracy, efficiency, cost-effectiveness. Experimental results a leaves dataset show impressive 99.6% accuracy. proposed framework outperforms existing methods, offering robust scalable solution for detection farming, contributing more sustainable practices.

Язык: Английский

Процитировано

0
Automated classification of thyroid disease using deep learning with neuroevolution model training DOI

Mohammad Rashid Dubayan,

Sara Ershadi-Nasab, Mariam Zomorodi‐Moghadam

и другие.

Engineering Applications of Artificial Intelligence, Год журнала: 2025, Номер 146, С. 110209 - 110209

Опубликована: Фев. 13, 2025

Язык: Английский

Процитировано

0
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

Процитировано

0