Results 51 to 60 of about 10,458 (157)

A Robust Stacking-Based Ensemble Model for Predicting Cardiovascular Diseases

AI
Background/Objectives: Cardiovascular diseases (CVDs) remain the primary cause of mortality worldwide, underscoring the critical importance of developing accurate early prediction models.
Hayat Bihri, Lalla Amina Charaf, Salma Azzouzi, My El Hassan Charaf   +3 more
doaj   +1 more source

Deep learning and machine learning predictive models for neurological function after interventional embolization of intracranial aneurysms

Frontiers in Neurology
ObjectiveThe objective of this study is to develop a model to predicts the postoperative Hunt-Hess grade in patients with intracranial aneurysms by integrating radiomics and deep learning technologies, using preoperative CTA imaging data.
Yan Peng, Yiren Wang, Yiren Wang, Zhongjian Wen, Zhongjian Wen, Hongli Xiang, Hongli Xiang, Ling Guo, Lei Su, Yongcheng He, Haowen Pang, Ping Zhou, Ping Zhou, Ping Zhou, Xiang Zhan   +14 more
doaj   +1 more source

Comparative Performance Analysis of Heterogeneous Ensemble Learning Models for Multi-Satellite Fusion GNSS-IR Soil Moisture Retrieval

Land
Given the complexity of near-surface soil moisture retrieval, a single machine learning algorithm often struggles to capture the intricate relationships among multiple features, resulting in limited generalization and robustness.
Yao Jiang, Rui Zhang, Hang Jiang, Bo Zhang, Kangyi Chen, Jichao Lv, Jie Chen, Yunfan Song   +7 more
doaj   +1 more source

An enhanced machine learning approach with stacking ensemble learner for accurate liver cancer diagnosis using feature selection and gene expression data

Healthcare Analytics
Liver cancer is a significant global health concern, necessitating accurate and timely diagnosis for effective treatment. Machine learning approaches have emerged as promising tools for improving liver cancer classification using gene expression data in ...
Amena Mahmoud, Eiko Takaoka
doaj   +1 more source

A Stacking Ensemble Machine Learning Model for Emergency Call Forecasting

IEEE Access
One of the greatest challenges of Emergency medical services providers is to handle the large number of Emergency Medical Service (EMS) calls coming from the population. An accurate forecast of EMS calls is involved in ambulance fleet dispatching and routing to minimize response times to emergency calls and enhance the efficacy of assistance.
Talotsing Gaelle Patricia Megouo, Samuel Pierre   +1 more
openaire   +3 more sources

Early mortality prediction after severe trauma using ensemble machine learning: a single-center retrospective study

Frontiers in Public Health
Early identification of trauma patients at risk of in-hospital death is essential for guiding time-critical resuscitation and operative strategies. We developed and evaluated a multi-model machine learning framework that integrates vital signs, and ...
Lijing Ling, Jin Ma, Xiaohua Xia, Hua Yuan, Shifang Liu, Yuelan Zhu   +5 more
doaj   +1 more source

Toward an Accurate Liver Disease Prediction Based on Two-Level Ensemble Stacking Model

IEEE Access
The difficulty of detecting liver disease at an early stage goes back to its limited number of symptoms. In this study, single and ensemble machine learning (ML) algorithms are applied to the Indian Liver Patient Dataset (ILPD) dataset, and their results,
Marghany Hassan Mohamed, Botheina Hussein Ali, Ahmed Ibrahim Taloba, Ahmad O. Aseeri, Mohamed Abd Elaziz, Shaker El-Sappagah, Nora Mahmoud El-Rashidy   +6 more
doaj   +1 more source

A Financial Fraud Prediction Framework Based on Stacking Ensemble Learning

Systems
With the rapid development of the capital market, financial fraud cases are becoming increasingly common. The evolving fraud strategies pose significant threats to financial regulation, market order, and the interests of ordinary investors. In order to combine the generalization performance of different machine learning methods and improve the ...
Shanshan Zhu, Haotian Wu, Eric W. T. Ngai, Jifan Ren, Daojing He, Tengyun Ma, Yubin Li   +6 more
openaire   +2 more sources

Lifestyle data-based multiclass obesity prediction with interpretable ensemble models incorporating SHAP and LIME analysis

Scientific Reports
Obesity is a major public health concern. Predicting obesity risk from lifestyle data can guide targeted interventions, but current models remain limited.
Shahid Mohammad Ganie, Pijush Kanti Dutta Pramanik, Zhongming Zhao   +2 more
doaj   +1 more source

Fake Instagram Account Detection Using Single and Stacking Ensemble Models on Integrated Static and Temporal Features

Wasit Journal for Pure Sciences
This research examines the detection of fake Instagram accounts by comparing single machine learning classifiers with a stacking ensemble model applied to integrated static and temporal features.
Samar Sadeq Hassoun, Nahla A. Flayh
doaj   +1 more source