ArticleInternational journal of environmental research and public health2022
A Machine Learning Classifier for Predicting Stable MCI Patients Using Gene Biomarkers.
Article in International journal of environmental research and public health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- Quantification of identifying cognitive impairment using olfactory-stimulated functional near-infrared spectroscopy with machine learning: a post hoc analysis of a diagnostic trial and validation of an external additional trial.Alzheimer's research & therapy · 2023Trial
- LASSO-HHO two-stage hybrid gene selection framework for accurate Alzheimer's disease diagnosis.Scientific reports · 2026Article
- Development and validation of a risk prediction model for mild cognitive impairment in older Chinese adults with chronic diseases.BMC geriatrics · 2026Observational
- Innovations in Alzheimer's disease diagnostic technologies: clinical prospects of novel biomarkers, multimodal integration, and non-invasive detection.Frontiers in neurology · 2025Review
- A review of AI-based radiogenomics in neurodegenerative disease.Frontiers in big data · 2025Review
- Development and interpretation of a machine learning predictive model for early cognitive impairment in hypertension associated with environmental factors.Frontiers in cardiovascular medicine · 2025Article
- Research progress in predicting the conversion from mild cognitive impairment to Alzheimer's disease via multimodal MRI and artificial intelligence.Frontiers in neurology · 2025Review
- Enhancing early detection of Alzheimer's disease through hybrid models based on feature fusion of multi-CNN and handcrafted features.Scientific reports · 2024Article
- Predicting poor performance on cognitive tests among older adults using wearable device data and machine learning: a feasibility study.npj aging · 2024Article
- A novel multitask learning algorithm for tasks with distinct chemical space: zebrafish toxicity prediction as an example.Journal of cheminformatics · 2024Article
- Machine Learning Model for Mild Cognitive Impairment Stage Based on Gait and MRI Images.Brain sciences · 2024Article
- Subject Harmonization of Digital Biomarkers: Improved Detection of Mild Cognitive Impairment from Language Markers.Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing · 2024Article
- Article
- The Technology-Oriented Pathway for Auxiliary Diagnosis in the Digital Health Age: A Self-Adaptive Disease Prediction Model.International journal of environmental research and public health · 2022Article
- Big Data, Decision Models, and Public Health.International journal of environmental research and public health · 2022Article
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Authors and funding
3 authors.
Funding
Abstract
Alzheimer's disease (AD) is a neurodegenerative disorder with an insidious onset and irreversible condition. Patients with mild cognitive impairment (MCI) are at high risk of converting to AD. Early diagnosis of unstable MCI patients is therefore vital for slowing the progression to AD. However, current diagnostic methods are either highly invasive or expensive, preventing their wide applications. Developing low-invasive and cost-efficient screening methods is desirable as the first-tier approach for identifying unstable MCI patients or excluding stable MCI patients. This study developed feature selection and machine learning algorithms to identify blood-sample gene biomarkers for predicting stable MCI patients. Two datasets obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database were utilized to conclude 29 genes biomarkers (31 probes) for predicting stable MCI patients. A random forest-based classifier performed well with area under the receiver operating characteristic curve (AUC) values of 0.841 and 0.775 for cross-validation and test datasets, respectively. For patients with a prediction score greater than 0.9, an excellent concordance of 97% was obtained, showing the usefulness of the proposed method for identifying stable MCI patients. In the context of precision medicine, the proposed prediction model is expected to be useful for identifying stable MCI patients and providing medical doctors and patients with new first-tier diagnosis options.
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