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Ensemble Learning

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Ensemble Learning Approaches for Automatic Detection of Chronic Kidney Disease Stages during Sleep.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
This study investigates the use of ensemble learning methods for the automatic detection of chronic kidney disease (CKD) stages during sleep. We applied and evaluated four ensemble learning approaches-CatBoost, random forest, XGBoost, and LightGBM-to...

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...

Automated Autism Assessment With Multimodal Data and Ensemble Learning: A Scalable and Consistent Robot-Enhanced Therapy Framework.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Navigating the complexities of Autism Spectrum Disorder (ASD) diagnosis and intervention requires a nuanced approach that addresses both the inherent variability in therapeutic practices and the imperative for scalable solutions. This paper presents ...

Comparative performance of multiple ensemble learning models for preoperative prediction of tumor deposits in rectal cancer based on MR imaging.

Scientific reports
Ensemble learning can effectively mitigate the risk of model overfitting during training. This study aims to evaluate the performance of ensemble learning models in predicting tumor deposits in rectal cancer (RC) and identify the optimal model for pr...

Interpretable lung cancer risk prediction using ensemble learning and XAI based on lifestyle and demographic data.

Computational biology and chemistry
Lung cancer is a leading cause of cancer-related death worldwide. The early and accurate detection of lung cancer is crucial for improving patient outcomes. Traditional predictive models often lack the accuracy and interpretability required in clinic...

MCNEL: A multi-scale convolutional network and ensemble learning for Alzheimer's disease diagnosis.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Alzheimer's disease (AD) significantly threatens community well-being and healthcare resource allocation due to its high incidence and mortality. Therefore, early detection and intervention are crucial for reducing AD-relate...

Improving genetic variant identification for quantitative traits using ensemble learning-based approaches.

BMC genomics
BACKGROUND: Genome-wide association studies (GWAS) are rapidly advancing due to the improved resolution and completeness provided by Telomere-to-Telomere (T2T) and pangenome assemblies. While recent advancements in GWAS methods have primarily focused...

Ensemble learning based on matrix completion improves microbe-disease association prediction.

Briefings in bioinformatics
Microbes have a profound impact on human health. Identifying disease-associated microbes would provide helpful guidance for drug development and disease treatment. Through an enormous experimental effort, limited disease-associated microbes have been...

A deep ensemble learning approach for squamous cell classification in cervical cancer.

Scientific reports
Cervical cancer, arising from the cells of the cervix, the lower segment of the uterus connected to the vagina-poses a significant health threat. The microscopic examination of cervical cells using Pap smear techniques plays a crucial role in identif...

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...