ArticleSensors (Basel, Switzerland)2022
A Long Short-Term Memory Biomarker-Based Prediction Framework for Alzheimer's Disease.
Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed, 55 citations in OpenAlex.
- Intra-scale interaction and cross-scale fusion network for detecting the progression of neurodegeneration in Alzheimer's disease.Scientific reports · 2025Article
- Artificial Intelligence in Biomedicine: A Systematic Review from Nanomedicine to Neurology and Hepatology.Pharmaceutics · 2025Review
- IoMT driven Alzheimer's prediction model empowered with transfer learning and explainable AI approach in healthcare 5.0.Scientific reports · 2025Article
- Predicting Alzheimer's Disease Progression from Sparse Multimodal Data by NeuralODE Models.bioRxiv : the preprint server for biology · 2025Article
- Artificial Intelligence in Alzheimer's Disease Diagnosis and Prognosis Using PET-MRI: A Narrative Review of High-Impact Literature Post-Tauvid Approval.Journal of clinical medicine · 2025Review
- Automatic pain classification in older patients with hip fracture based on multimodal information fusion.Scientific reports · 2025Article
- Inequalities in Mild Cognitive Impairment Risk Among Chinese Middle-Aged and Older Adults: Insights from an Integrated Learning Model.Risk management and healthcare policy · 2025Article
- GAN-enhanced deep learning for improved Alzheimer's disease classification and longitudinal brain change analysis.Frontiers in medicine · 2025Article
- Enhancing early detection of Alzheimer's disease through hybrid models based on feature fusion of multi-CNN and handcrafted features.Scientific reports · 2024Article
- Predicting Four-Year's Alzheimer's Disease Onset Using Longitudinal Neurocognitive Tests and MRI Data Using Explainable Deep Convolutional Neural Networks.Journal of Alzheimer's disease : JAD · 2024Article
- Normalized group activations based feature extraction technique using heterogeneous data for Alzheimer's disease classification.PeerJ. Computer science · 2024Article
- Artificial Cognition for Detection of Mental Disability: A Vision Transformer Approach for Alzheimer's Disease.Healthcare (Basel, Switzerland) · 2023Article
- Computer aided progression detection model based on optimized deep LSTM ensemble model and the fusion of multivariate time series data.Scientific reports · 2023Article
- An Approach to Binary Classification of Alzheimer's Disease Using LSTM.Bioengineering (Basel, Switzerland) · 2023Article
- Bidirectional Neural Network Model for Glaucoma Progression Prediction.Journal of personalized medicine · 2023Article
- Sensor Data Fusion Based on Deep Learning for Computer Vision Applications and Medical Applications.Sensors (Basel, Switzerland) · 2022Article
Corrections and comments
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Authors and funding
8 authors at 5 institutions in 4 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The early prediction of Alzheimer's disease (AD) can be vital for the endurance of patients and establishes as an accommodating and facilitative factor for specialists. The proposed work presents a robotized predictive structure, dependent on machine learning (ML) methods for the forecast of AD. Neuropsychological measures (NM) and magnetic resonance imaging (MRI) biomarkers are deduced and passed on to a recurrent neural network (RNN). In the RNN, we have used long short-term memory (LSTM), and the proposed model will predict the biomarkers (feature vectors) of patients after 6, 12, 21 18, 24, and 36 months. These predicted biomarkers will go through fully connected neural network layers. The NN layers will then predict whether these RNN-predicted biomarkers belong to an AD patient or a patient with a mild cognitive impairment (MCI). The developed methodology has been tried on an openly available informational dataset (ADNI) and accomplished an accuracy of 88.24%, which is superior to the next-best available algorithms.
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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.