AIMC Topic: Ensemble Learning

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Ensemble learning to predict short birth interval among reproductive-age women in Ethiopia: evidence from EDHS 2016-2019.

BMC pregnancy and childbirth
BACKGROUND: A birth interval of less than 33 months was considered short, and in low- income countries like Ethiopia, a short birth interval is the primary cause of approximately 822 maternal deaths every day. Due to that this study aimed to predict ...

Integrating neuroscience and artificial intelligence: EEG analysis using ensemble learning for diagnosis Alzheimer's disease and frontotemporal dementia.

Journal of neuroscience methods
BACKGROUND: Alzheimer's disease (AD) and frontotemporal dementia (FTD) are both progressive neurological disorders that affect the elderly. Distinguishing between individuals suffering from these two diseases in the early stages can be quite challeng...

Enhanced brain tumor detection and segmentation using densely connected convolutional networks with stacking ensemble learning.

Computers in biology and medicine
- Brain tumors (BT), both benign and malignant, pose a substantial impact on human health and need precise and early detection for successful treatment. Analysing magnetic resonance imaging (MRI) image is a common method for BT diagnosis and segmenta...

A Dynamic Adaptive Ensemble Learning Framework for Noninvasive Mild Cognitive Impairment Detection: Development and Validation Study.

JMIR medical informatics
BACKGROUND: The prompt and accurate identification of mild cognitive impairment (MCI) is crucial for preventing its progression into more severe neurodegenerative diseases. However, current diagnostic solutions, such as biomarkers and cognitive scree...

Automatic segmentation of MRI images for brain radiotherapy planning using deep ensemble learning.

Biomedical physics & engineering express
This study aimed to develop and evaluate an efficient method to automatically segment T1- and T2-weighted brain magnetic resonance imaging (MRI) images. We specifically compared the segmentation performance of individual convolutional neural network ...

Semi-Supervised Detection Model Based on Adaptive Ensemble Learning for Medical Images.

IEEE transactions on neural networks and learning systems
Introducing deep learning technologies into the medical image processing field requires accuracy guarantee, especially for high-resolution images relayed through endoscopes. Moreover, works relying on supervised learning are powerless in the case of ...

DDEvENet: Evidence-based ensemble learning for uncertainty-aware brain parcellation using diffusion MRI.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
In this study, we developed an Evidential Ensemble Neural Network based on Deep learning and Diffusion MRI, namely DDEvENet, for anatomical brain parcellation. The key innovation of DDEvENet is the design of an evidential deep learning framework to q...

Improving groundwater quality predictions in semi-arid regions using ensemble learning models.

Environmental science and pollution research international
Groundwater resources constitute one of the primary sources of freshwater in semi-arid and arid climates. Monitoring the groundwater quality is an essential component of environmental management. In this study, a comprehensive comparison was conducte...

An ensemble learning method combined with multiple feature representation strategies to predict lncRNA subcellular localizations.

Computational biology and chemistry
Long non-coding RNAs (lncRNAs) are strongly associated with cellular physiological mechanisms and implicated in the numerous diseases. By exploring the subcellular localizations of lncRNAs, we can not only gain crucial insights into the molecular mec...

Ensemble learning-based radiomics model for discriminating brain metastasis from glioblastoma.

European journal of radiology
OBJECTIVE: Differentiating between brain metastasis (BM) and glioblastoma (GBM) preoperatively is challenging due to their similar imaging features on conventional brain MRI. This study aimed to enhance diagnostic accuracy through a machine learning ...