ArticleFrontiers in psychiatry2020
Ensemble Learning of Convolutional Neural Network, Support Vector Machine, and Best Linear Unbiased Predictor for Brain Age Prediction: ARAMIS Contribution to the Predictive Analytics Competition 2019 Challenge.
Article in Frontiers in psychiatry, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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21 citing papers in PubMed.
- A Lightweight ScaleDense-Transformer Framework with Auxiliary Quantum-Inspired Bottleneck Module for Whole-Lifespan Brain Age Prediction.Brain sciences · 2026Article
- Aging beyond diagnosis: the MRI brain age gap across disorders.GeroScience · 2026Review
- Applying machine-learning and deep-learning to predict depression from brain MRI and identify depression-related brain biology.Translational psychiatry · 2026Article
- Biological age prediction using a DNN model based on pathways of steroidogenesis.Science advances · 2025Article
- Disparities in accelerated brain aging in recent-onset and chronic schizophrenia.Psychological medicine · 2025Article
- Automatic rating of incomplete hippocampal inversions evaluated across multiple cohorts.ArXiv · 2025Article
- Development and validation of an ensemble learning risk model for sepsis after abdominal surgery.Archives of medical science : AMS · 2025Article
- Machine Learning and Deep Learning Approaches in Lifespan Brain Age Prediction: A Comprehensive Review.Tomography (Ann Arbor, Mich.) · 2024Review
- Deep Learning-based Brain Age Prediction in Patients With Schizophrenia Spectrum Disorders.Schizophrenia bulletin · 2024Article
- Brain-age prediction: Systematic evaluation of site effects, and sample age range and size.Human brain mapping · 2024Article
- A ResNet mini architecture for brain age prediction.Scientific reports · 2024Article
- Tactics analysis and evaluation of women football team based on convolutional neural network.Scientific reports · 2024Article
- Article
- Anatomically interpretable deep learning of brain age captures domain-specific cognitive impairment.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
- Accelerated functional brain aging in major depressive disorder: evidence from a large scale fMRI analysis of Chinese participants.Translational psychiatry · 2022Article
- Mind the gap: Performance metric evaluation in brain-age prediction.Human brain mapping · 2022Article
- Multimodal brain age prediction fusing morphometric and imaging data and association with cardiovascular risk factors.Frontiers in neurology · 2022Article
- Data-driven approaches to neuroimaging biomarkers for neurological and psychiatric disorders: emerging approaches and examples.Current opinion in neurology · 2021Review
- Improving Individual Brain Age Prediction Using an Ensemble Deep Learning Framework.Frontiers in psychiatry · 2021Article
- Longitudinal Analysis of Brain-Predicted Age in Amnestic and Non-amnestic Sporadic Early-Onset Alzheimer's Disease.Frontiers in aging neuroscience · 2021Article
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10 authors.
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Abstract
We ranked third in the Predictive Analytics Competition (PAC) 2019 challenge by achieving a mean absolute error (MAE) of 3.33 years in predicting age from T1-weighted MRI brain images. Our approach combined seven algorithms that allow generating predictions when the number of features exceeds the number of observations, in particular, two versions of best linear unbiased predictor (BLUP), support vector machine (SVM), two shallow convolutional neural networks (CNNs), and the famous ResNet and Inception V1. Ensemble learning was derived from estimating weights via linear regression in a hold-out subset of the training sample. We further evaluated and identified factors that could influence prediction accuracy: choice of algorithm, ensemble learning, and features used as input/MRI image processing. Our prediction error was correlated with age, and absolute error was greater for older participants, suggesting to increase the training sample for this subgroup. Our results may be used to guide researchers to build age predictors on healthy individuals, which can be used in research and in the clinics as non-specific predictors of disease status.
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