ArticleBrain, behavior, & immunity - health2025
Evaluation of machine learning models for the prediction of Alzheimer's: In search of the best performance.
Article in Brain, behavior, & immunity - health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
5 citing papers in PubMed.
- Feature Selection Analysis and Robustness to Missing Data for Sensor-Based Prognostic Modeling of Alzheimer's Disease Progression.Sensors (Basel, Switzerland) · 2026Article
- Centralized Review of Alzheimer's Disease and Related Dementias Biomedical Repositories and Computational Methods.Bioengineering (Basel, Switzerland) · 2026Review
- AutoML-Multiverse: An Instability-Aware Framework for Quantifying Analytic Variability in Alzheimer's Disease Machine-Learning Studies.medRxiv : the preprint server for health sciences · 2026Article
- Interpretable Alzheimer's Diagnosis via Multimodal Fusion of Regional Brain Experts.Proceedings. IEEE International Conference on Healthcare Informatics · 2026Article
- A lightweight machine learning tool for Alzheimer's disease prediction.Alzheimer's & dementia (Amsterdam, Netherlands)Article
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
No grant is acknowledged in the PubMed record.
Abstract
Alzheimer's is a progressive and degenerative disease affecting millions worldwide, incapacitating them physically and cognitively. This study aims to perform a comparative analysis of Machine Learning models to determine the model with the best performance in predicting Alzheimer's disease. The models used were Random Forest (RF), Adaptive Boosting (AdaBoost), Support Vector Machine (SVM), K-nearest Neighbors (KNN), and Logistic Regression (LR). Two datasets called OASIS were used to train the models, the first one had a total of 436 records and 12 variables, while the second one stored 373 records and 15 variables. The article's content is divided into six main sections: introduction, literature review, methodological approach, results, discussions, and conclusions. After processing and pooling the datasets, RF, SVM, and LR proved the best predictors, achieving 96% accuracy, precision, sensitivity, and F1 score. This study highlights the efficacy of RF, SVM, and LR in predicting Alzheimer's disease, offering a significant advance toward understanding and management of this disease, which supports the relevance of implementing these models in future research and clinical applications.
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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.