ArticleSensors (Basel, Switzerland)2022
Voting Ensemble Approach for Enhancing Alzheimer's Disease Classification.
Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 3 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
12 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- From Single-Modal to Multi-Modal Artificial Intelligence in Alzheimer's Disease: A Systematic Review of Databases, Modalities, Diagnostic Performance, and Clinical Translation Challenges.Sensors (Basel, Switzerland) · 2026Pooled it
- Computer-Aided Decision Support Systems of Alzheimer's Disease Diagnosis - A Systematic Review.Current medical imaging · 2025Pooled it
- Revolutionizing Lung Cancer Detection: A High-Accuracy Machine Learning Framework for Early Diagnosis.BioMed research international · 2025Pooled it
- Comparative analysis of supervised and ensemble models with unsupervised exploration for alzheimer's disease prediction.Scientific reports · 2026Article
- A Weighted Voting Approach for Traditional Chinese Medicine Formula Classification Using Large Language Models: Algorithm Development and Validation Study.JMIR medical informatics · 2025Article
- Ensemble Learning-Based Alzheimer's Disease Classification Using Electroencephalogram Signals and Clock Drawing Test Images.Sensors (Basel, Switzerland) · 2025Article
- A hybrid transformer-based approach for early detection of Alzheimer's disease using MRI images.BioImpacts : BI · 2025Article
- Review
- Deep Learning for Alzheimer's Disease Prediction: A Comprehensive Review.Diagnostics (Basel, Switzerland) · 2024Review
- Machine learning classification of polycystic ovary syndrome based on radial pulse wave analysis.BMC complementary medicine and therapies · 2023Article
- An Ensemble of Deep Learning Object Detection Models for Anatomical and Pathological Regions in Brain MRI.Diagnostics (Basel, Switzerland) · 2023Article
- Leveraging ensemble learning for predicting 30-day readmission in heart failure ICU patients.Digital healthArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
Alzheimer's disease is dementia that impairs one's thinking, behavior, and memory. It starts as a moderate condition affecting areas of the brain that make it challenging to retain recently learned information, causes mood swings, and causes confusion regarding occasions, times, and locations. The most prevalent type of dementia, called Alzheimer's disease (AD), causes memory-related problems in patients. A precise medical diagnosis that correctly classifies AD patients results in better treatment. Currently, the most commonly used classification techniques extract features from longitudinal MRI data before creating a single classifier that performs classification. However, it is difficult to train a reliable classifier to achieve acceptable classification performance due to limited sample size and noise in longitudinal MRI data. Instead of creating a single classifier, we propose an ensemble voting method that generates multiple individual classifier predictions and then combines them to develop a more accurate and reliable classifier. The ensemble voting classifier model performs better in the Open Access Series of Imaging Studies (OASIS) dataset for older adults than existing methods in important assessment criteria such as accuracy, sensitivity, specificity, and AUC. For the binary classification of with dementia and no dementia, an accuracy of 96.4% and an AUC of 97.2% is attained.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.