ArticlePsychiatry investigation2021
Development of Random Forest Algorithm Based Prediction Model of Alzheimer's Disease Using Neurodegeneration Pattern.
Article in Psychiatry investigation, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed, 38 citations in OpenAlex.
- A collaborative multi-view fusion framework with Deep Forest for multi-class diagnosis of Alzheimer's disease.Frontiers in aging neuroscience · 2026Article
- NeuroNet-AD: A Multimodal Deep Learning Framework for Multiclass Alzheimer's Disease Diagnosis.Bioengineering (Basel, Switzerland) · 2025Article
- Nanomedicine-Driven Modulation of the Gut-Brain Axis: Innovative Approaches to Managing Chronic Inflammation in Alzheimer's and Parkinson's Disease.International journal of molecular sciences · 2025Review
- A Meta-Learning-Based Ensemble Model for Explainable Alzheimer's Disease Diagnosis.Diagnostics (Basel, Switzerland) · 2025Article
- A noninvasive realtime body position monitoring system for the entire course of tumor radiotherapy.NPJ digital medicine · 2025Article
- Prediction of childbearing tendency in women on the verge of marriage using machine learning techniques.Scientific reports · 2024Article
- Multiparametric hippocampal signatures for early diagnosis of Alzheimer's disease usingCNS neuroscience & therapeutics · 2024Article
- A Review on the Use of Modern Computational Methods in Alzheimer's Disease-Detection and Prediction.Current Alzheimer research · 2024Review
- Impact of brain segmentation methods on regional metabolism quantification inEJNMMI research · 2023Article
- An eXplainability Artificial Intelligence approach to brain connectivity in Alzheimer's disease.Frontiers in aging neuroscience · 2023Article
- Prediction of Early Alzheimer Disease by Hippocampal Volume Changes under Machine Learning Algorithm.Computational and mathematical methods in medicine · 2022Article
Corrections and comments
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Authors and funding
13 authors at 5 institutions in 2 countries.
Funding
Abstract
objectiveAlzheimer's disease (AD) is the most common type of dementia and the prevalence rapidly increased as the elderly population increased worldwide. In the contemporary model of AD, it is regarded as a disease continuum involving preclinical stage to severe dementia. For accurate diagnosis and disease monitoring, objective index reflecting structural change of brain is needed to correctly assess a patient's severity of neurodegeneration independent from the patient's clinical symptoms. The main aim of this paper is to develop a random forest (RF) algorithm-based prediction model of AD using structural magnetic resonance imaging (MRI).
methodsWe evaluated diagnostic accuracy and performance of our RF based prediction model using newly developed brain segmentation method compared with the Freesurfer's which is a commonly used segmentation software.
resultsOur RF model showed high diagnostic accuracy for differentiating healthy controls from AD and mild cognitive impairment (MCI) using structural MRI, patient characteristics, and cognitive function (HC vs. AD 93.5%, AUC 0.99; HC vs. MCI 80.8%, AUC 0.88). Moreover, segmentation processing time of our algorithm (<5 minutes) was much shorter than of Freesurfer's (6-8 hours).
conclusionOur RF model might be an effective automatic brain segmentation tool which can be easily applied in real clinical practice.
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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.