ArticlePloS one2019
Random forest prediction of Alzheimer's disease using pairwise selection from time series data.
Article in PloS one, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers.
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Who cites it
35 citing papers in PubMed.
- A multilayer multimodal detection and prediction model based on explainable artificial intelligence for Alzheimer's disease.Scientific reports · 2021Trial
- Comparative Analysis by Machine Learning of Geriatric Frailty and Alzheimer's Disease Classification Using Independent Datasets.Life (Basel, Switzerland) · 2026Article
- Artificial Intelligence-Enhanced Multi-Algorithm R Shiny Application for Predictive Modeling and Analytics: Case Study of Alzheimer Disease Diagnostics.JMIR aging · 2025Article
- Modeling epithelial wound closure dynamics with AI: A comparative study across cell types.Regenerative therapy · 2025Article
- Comparing machine learning classifier models in discriminating cognitively unimpaired older adults from three clinical cohorts in the Alzheimer's disease spectrum: demonstration analyses in the COMPASS-ND study.Frontiers in aging neuroscience · 2025Article
- HiMAL: Multimodal Hierarchical Multi-task Auxiliary Learning framework for predicting Alzheimer's disease progression.JAMIA open · 2024Article
- Lipoproteins and metabolites in diagnosing and predicting Alzheimer's disease using machine learning.Lipids in health and disease · 2024Article
- Integrating Demographics and Imaging Features for Various Stages of Dementia Classification: Feed Forward Neural Network Multi-Class Approach.Biomedicines · 2024Article
- An explainable machine learning approach for Alzheimer's disease classification.Scientific reports · 2024Article
- Contextualizing injury severity from occupational accident reports using an optimized deep learning prediction model.PeerJ. Computer science · 2024Article
- Breast cancer prediction using different machine learning methods applying multi factors.Journal of cancer research and clinical oncology · 2023Article
- Computer aided progression detection model based on optimized deep LSTM ensemble model and the fusion of multivariate time series data.Scientific reports · 2023Article
- Assessing the transportability of clinical prediction models for cognitive impairment using causal models.BMC medical research methodology · 2023Article
- Article
- X chromosome-wide association study of quantitative biomarkers from the Alzheimer's Disease Neuroimaging Initiative study.Frontiers in aging neuroscience · 2023Article
- Autism screening: an unsupervised machine learning approach.Health information science and systems · 2022Article
- Use of blood pressure measurements extracted from the electronic health record in predicting Alzheimer's disease: A retrospective cohort study at two medical centers.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2022Article
- Occupational Injury Risk Mitigation: Machine Learning Approach and Feature Optimization for Smart Workplace Surveillance.International journal of environmental research and public health · 2022Article
- The influence of meteorological variables and lockdowns on COVID-19 cases in urban agglomerations of Indian cities.Stochastic environmental research and risk assessment : research journal · 2022Article
- Article
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Authors and funding
4 authors.
Funding
Abstract
Time-dependent data collected in studies of Alzheimer's disease usually has missing and irregularly sampled data points. For this reason time series methods which assume regular sampling cannot be applied directly to the data without a pre-processing step. In this paper we use a random forest to learn the relationship between pairs of data points at different time separations. The input vector is a summary of the time series history and it includes both demographic and non-time varying variables such as genetic data. To test the method we use data from the TADPOLE grand challenge, an initiative which aims to predict the evolution of subjects at risk of Alzheimer's disease using demographic, physical and cognitive input data. The task is to predict diagnosis, ADAS-13 score and normalised ventricles volume. While the competition proceeds, forecasting methods may be compared using a leaderboard dataset selected from the Alzheimer's Disease Neuroimaging Initiative (ADNI) and with standard metrics for measuring accuracy. For diagnosis, we find an mAUC of 0.82, and a classification accuracy of 0.73 compared with a benchmark SVM predictor which gives mAUC = 0.62 and BCA = 0.52. The results show that the method is effective and comparable with other methods.
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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.