ArticleJournal of the Chinese Medical Association : JCMA2025
Comparison of machine learning algorithms for automatic prediction of Alzheimer disease.
Article in Journal of the Chinese Medical Association : JCMA, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed.
- A hybrid deep learning framework based on VGG19 and U-Net for accurate brain tumor segmentation in MRI images.Magnetic resonance letters · 2026Article
- Artificial Intelligence for Alzheimer's Disease Diagnosis: From Traditional Machine Learning to Large Language Models.Biosensors · 2026Review
- Feature Selection Analysis and Robustness to Missing Data for Sensor-Based Prognostic Modeling of Alzheimer's Disease Progression.Sensors (Basel, Switzerland) · 2026Article
- Comparative Analysis of Four Commercially Available Artificial Intelligence Software for Brain Volumetry.Diagnostics (Basel, Switzerland) · 2026Article
- A hybrid CNN-LSTM approach for large-scale Twitter sentiment analysis with explainable artificial intelligence.Scientific reports · 2026Article
- Enhancing prediction accuracy for Parkinson's disease using advanced machine learning models.Scientific reports · 2026Article
- Machine learning models for smart grid stability prediction: a comparative analysis.Scientific reports · 2026Article
- Limitations of MMSE in Cognitive Assessment: Revealing Latent Risk via Structural Brain Atrophy.Life (Basel, Switzerland) · 2026Article
- A hybrid machine learning approach for reliably predicting surface roughness in CNC turning operations.Scientific reports · 2026Article
- Classification of pulmonary diseases using machine learning and deep learning models on GLI-2012 standardized spirometry features.BMC medical informatics and decision making · 2026Article
- An explainable machine learning framework utilizing ultrasound radiomics for the preoperative differentiation between granulomatous lobular mastitis and breast cancer.Frontiers in oncology · 2026Article
- Reliability and diagnostic performance of an automated MRI-based classifier compared with radiologists in Alzheimer's disease.Frontiers in neuroinformatics · 2026Article
- HyperTransFusion: a hypernetwork transformer with black winged kite optimization for multimodal early Alzheimer's disease diagnosis.Frontiers in digital health · 2026Article
- Identification of Peristomal Skin Alterations Using Convolutional Artificial Neural Networks.Nursing research and practice · 2026Article
- Interpretable machine learning integrating intra-/peritumoral CT radiomics and serum indicators for predicting poorly differentiated esophageal squamous cell carcinoma.Frontiers in oncology · 2026Article
- Scalable and objective wound infection screening from clinical images using deep learning.Frontiers in public health · 2026Article
- Fusion of transfer learning models for detection of alzheimer's disease using bidirectional long short-term memory with equilibrium optimization algorithm.Scientific reports · 2025Article
- OASIS-SB: a sex-balanced, distribution-preserving, synthetic phenotypic dataset for bias-resilient clinical prediction.Frontiers in computational neuroscience · 2025Article
- A lightweight CNN for colon cancer tissue classification and visualization.Frontiers in oncology · 2025Article
- GMANet: a CPU-only ultra-lightweight 3D CNN for real-time glioma triage in resource-limited environments.Frontiers in neurologyArticle
Corrections and comments
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2 authors.
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Abstract
backgroundAlzheimer disease is a progressive neurological disorder marked by irreversible memory loss and cognitive decline. Traditional diagnostic tools, such as intracranial volume assessments, electroencephalography (EEG) signals, and brain magnetic resonance imaging (MRI), have shown utility in detecting the disease. However, artificial intelligence (AI) offers promise for automating this process, potentially enhancing diagnostic accuracy and accessibility.
methodsIn this study, various machine learning models were used to detect Alzheimer disease, including K-nearest neighbor regression, support vector machines (SVM), AdaBoost regression, and logistic regression. A neural network was constructed and validated using data from 150 participants in the University of Washington's Alzheimer's Disease Research Center (Open Access Imaging Studies Series [OASIS] dataset). Cross-validation was also performed on the Alzheimer Disease Neuroimaging Initiative (ADNI) dataset to assess the robustness of the models.
resultsAmong the models tested, K-nearest neighbor regression achieved the highest accuracy, reaching 97.33%. The cross-validation on the ADNI dataset further confirmed the effectiveness of the models, demonstrating satisfactory results in screening and diagnosing Alzheimer disease in a community-based sample.
conclusionThe findings indicate that AI-based models, particularly K-nearest neighbor regression, provide promising accuracy for the early detection of Alzheimer disease. This approach has potential for further development into practical diagnostic tools that could be applied in clinical and community settings.
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