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Ensemble of deep learning language models to support the creation of living systematic reviews for the COVID-19 literature
BackgroundThe COVID-19 pandemic has led to an unprecedented amount of scientific publications, growing at a pace never seen before. Multiple living...
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Automated cervical cell segmentation using deep ensemble learning
BackgroundCervical cell segmentation is a fundamental step in automated cervical cancer cytology screening. The aim of this study was to develop and...
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Deep transfer learning with fuzzy ensemble approach for the early detection of breast cancer
Breast Cancer is a significant global health challenge, particularly affecting women with higher mortality compared with other cancer types. Timely...
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An ensemble model for predicting dispositions of emergency department patients
ObjectiveThe healthcare challenge driven by an aging population and rising demand is one of the most pressing issues leading to emergency department...
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Improved liver disease prediction from clinical data through an evaluation of ensemble learning approaches
PurposeLiver disease causes two million deaths annually, accounting for 4% of all deaths globally. Prediction or early detection of the disease via...
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An Integrated Ensemble Network Model for Skin Abnormality Detection with Combined Textural Features
Melanoma is the most lethal of all skin cancers. This necessitates the need for a machine learning-driven skin cancer detection system to help...
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A stacked ensemble machine learning approach for the prediction of diabetes
ObjectivesDiabetes has become a leading cause of mortality in both developed and develo** countries, impacting a growing number of individuals...
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Comparison of mortality prediction models for road traffic accidents: an ensemble technique for imbalanced data
BackgroundInjuries caused by RTA are classified under the International Classification of Diseases-10 as ‘S00-T99’ and represent imbalanced samples...
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Improving the second-tier classification of methylmalonic acidemia patients using a machine learning ensemble method
IntroductionMethylmalonic acidemia (MMA) is a disorder of autosomal recessive inheritance, with an estimated prevalence of 1:50,000. First-tier...
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Risk adjustment for regional healthcare funding allocations with ensemble methods: an empirical study and interpretation
We experiment with recent ensemble machine learning methods in estimating healthcare costs, utilizing Finnish data containing rich individual-level...
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Identifying Leukoaraiosis with Mild Cognitive Impairment by Fusing Multiple MRI Morphological Metrics and Ensemble Machine Learning
Leukoaraiosis (LA) is strongly associated with impaired cognition and increased dementia risk. Determining effective and robust methods of...
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Automatic ovarian tumors recognition system based on ensemble convolutional neural network with ultrasound imaging
BackgroundUpon the discovery of ovarian cysts, obstetricians, gynecologists, and ultrasound examiners must address the common clinical challenge of...
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Predicting academic achievement from the collaborative influences of executive function, physical fitness, and demographic factors among primary school students in China: ensemble learning methods
BackgroundElevated levels of executive function and physical fitness play a pivotal role in sha** future quality of life. However, few studies have...
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Serum biomarker-based early detection of pancreatic ductal adenocarcinomas with ensemble learning
BackgroundEarlier detection of pancreatic ductal adenocarcinoma (PDAC) is key to improving patient outcomes, as it is mostly detected at advanced...
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SpatialWavePredict: a tutorial-based primer and toolbox for forecasting growth trajectories using the ensemble spatial wave sub-epidemic modeling framework
BackgroundDynamical mathematical models defined by a system of differential equations are typically not easily accessible to non-experts. However,...
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Estimating the volume of penumbra in rodents using DTI and stack-based ensemble machine learning framework
BackgroundThis study investigates the potential of diffusion tensor imaging (DTI) in identifying penumbral volume (PV) compared to the standard...
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A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability
BackgroundIntelligent cardiotocography (CTG) classification can assist obstetricians in evaluating fetal health. However, high classification...
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Automatic breast cancer diagnosis based on hybrid dimensionality reduction technique and ensemble classification
IntroductionFeature selection in the face of high-dimensional data can reduce overfitting and learning time, and at the same time improve the...
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A predictive ensemble classifier for the gene expression diagnosis of ASD at ages 1 to 4 years
Autism Spectrum Disorder (ASD) diagnosis remains behavior-based and the median age of diagnosis is ~52 months, nearly 5 years after its...
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LightGBM is an Effective Predictive Model for Postoperative Complications in Gastric Cancer: A Study Integrating Radiomics with Ensemble Learning
Postoperative complications of radical gastrectomy seriously affect postoperative recovery and require accurate risk prediction. Therefore, this...
