AIMC Topic: Ensemble Learning

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Ensemble Learning-Based Alzheimer's Disease Classification Using Electroencephalogram Signals and Clock Drawing Test Images.

Sensors (Basel, Switzerland)
Ensemble learning (EL), a machine learning technique that combines the results of multiple learning algorithms to obtain predicted values, aims to achieve better predictive performance than a single learning algorithm alone. Machine learning techniqu...

M3S-GRPred: a novel ensemble learning approach for the interpretable prediction of glucocorticoid receptor antagonists using a multi-step stacking strategy.

BMC bioinformatics
Accelerating drug discovery for glucocorticoid receptor (GR)-related disorders, including innovative machine learning (ML)-based approaches, holds promise in advancing therapeutic development, optimizing treatment efficacy, and mitigating adverse eff...

Prediction of Patient Visits for Skin Diseases through Enhanced Evolutionary Computation and Ensemble Learning.

Journal of medical systems
Skin diseases are an important global public health issue, causing significant health and psychological burdens. Predicting dermatology outpatient visits is essential for optimizing hospital resources and improving diagnosis and treatment methods. Ba...

Research on memory failure prediction based on ensemble learning.

PloS one
Timely prediction of memory failures is crucial for the stable operation of data centers. However, existing methods often rely on a single classifier, which can lead to inaccurate or unstable predictions. To address this, we propose a new ensemble mo...

Ensemble learning with explainable AI for improved heart disease prediction based on multiple datasets.

Scientific reports
Heart disease is one of the leading causes of death worldwide. Predicting and detecting heart disease early is crucial, as it allows medical professionals to take appropriate and necessary actions at earlier stages. Healthcare professionals can diagn...

A systematic literature review: exploring the challenges of ensemble model for medical imaging.

BMC medical imaging
BACKGROUND: Medical imaging has been essential and has provided clinicians with useful information about the human body to diagnose various health issues. Early diagnosis of diseases based on medical imaging can mitigate the risk of severe consequenc...

Automated pulmonary nodule classification from low-dose CT images using ERBNet: an ensemble learning approach.

Medical & biological engineering & computing
The aim of this study was to develop a deep learning method for analyzing CT images with varying doses and qualities, aiming to categorize lung lesions into nodules and non-nodules. This study utilized the lung nodule analysis 2016 challenge dataset....

Mitochondrial toxic prediction of marine alga toxins using a predictive model based on feature coupling and ensemble learning algorithms.

Toxicology mechanisms and methods
Alga toxins have recently emerged as environmental risk factors to multiple human health issues. Mitochondrial toxicity is an essential element in the field of ecotoxicology, it is necessary to screen and manage mitochondrial toxicants from common al...

Domain generalization for image classification based on simplified self ensemble learning.

PloS one
Domain generalization seeks to acquire knowledge from limited source data and apply it to an unknown target domain. Current approaches primarily tackle this challenge by attempting to eliminate the differences between domains. However, as cross-domai...

An ensemble learning model to predict lymph node metastasis in early gastric cancer.

Scientific reports
Lymph node metastasis is a critical factor for determining therapeutic strategies and assessing the prognosis of early gastric cancer. This work aimed to establish a more dependable predictive model for identify lymph node metastasis in early gastric...