ArticleDiagnostics (Basel, Switzerland)2024
Improving Alzheimer's Disease Prediction with Different Machine Learning Approaches and Feature Selection Techniques.
Article in Diagnostics (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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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.
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Who cites it
8 citing papers in PubMed.
- DiAbot: A Conversational AI System Coupling Large Language Models with an Interpretable Decision Tree for CDR-Style Dementia Screening.Bioengineering (Basel, Switzerland) · 2026Article
- Feature Selection Analysis and Robustness to Missing Data for Sensor-Based Prognostic Modeling of Alzheimer's Disease Progression.Sensors (Basel, Switzerland) · 2026Article
- Explainable machine learning for predicting longitudinal dementia status: Establishing a leakage-free benchmark.PLOS digital health · 2026Article
- Advances in AI-based diagnosis of Alzheimer's disease using MRI: a comprehensive survey.Frontiers in medicine · 2026Review
- Early Diagnosis of Alzheimer's Disease in Human Participants Using EEGConformer and Attention-Based LSTM During the Short Question Task.Diagnostics (Basel, Switzerland) · 2025Article
- Advancements in deep learning for early diagnosis of Alzheimer's disease using multimodal neuroimaging: challenges and future directions.Frontiers in neuroinformatics · 2025Review
- AlzheimerViT: harnessing lightweight vision transformer architecture for proactive Alzheimer's screening.Frontiers in medicine · 2025Article
- Optimizing Alzheimer's disease prediction through ensemble learning and feature interpretability with SHAP-based feature analysis.Alzheimer's & dementia (Amsterdam, Netherlands)Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Machine learning (ML) has increasingly been utilized in healthcare to facilitate disease diagnosis and prediction. This study focuses on predicting Alzheimer's disease (AD) through the development and comparison of ML models using Support Vector Machine (SVM), Random Forest (RF), and Logistic Regression (LR) algorithms. Additionally, feature selection techniques including Minimum Redundancy Maximum Relevance (mRMR) and Mutual Information (MI) were employed to enhance the model performance. The research methodology involved training and testing these models on the OASIS-2 dataset, evaluating their predictive accuracies. Notably, LR combined with mRMR achieved the highest accuracy of 99.08% in predicting AD. These findings underscore the efficacy of ML algorithms in AD prediction and highlight the utility of the feature selection methods in improving the model performance. This study contributes to the ongoing efforts to leverage ML for more accurate disease prognosis and underscores the potential of these techniques in advancing clinical decision-making.
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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.