Abstract Background Plasma biomarkers are associated with cognitive performance and decline in Alzheimer’s disease, making them promising for early detection. This study investigates their predictive value, combined non-invasive measures, non-demented individuals. Methods We developed a machine-learning approach incorporating plasma (A β 42/40, p-tau181, NfL), MRI, demographics, APOE4, assessments. Various models were designed to predict rates across domains assess relevance predicting...

Alzheimer's Disease Assessment Scale-Cognitive subscale (ADAS-Cog) is a neuropsychological tool that has been designed to assess the severity of cognitive symptoms dementia. Personalized prediction changes in ADAS-Cog scores could help timing therapeutic interventions dementia and at-risk populations. In present work, we compared single- multi-task learning approaches predict based on T1-weighted anatomical magnetic resonance imaging (MRI). contrast most machine learning-based methods...

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Simultaneous Localization and Mapping (SLAM) is one of the most challenging research areas within computer machine vision for automated scene commentary explanation. The SLAM technique has been a developing area in robotics context during recent years. By utilizing method robot can estimate different positions at distinct points time which indicate trajectory as well generate map environment. unique traits are estimating location building various types effective environment such indoor,...

Feature selection in noisy label scenarios remains an understudied topic. We propose a novel genetic algorithm-based approach, the Noise-Aware Multi-Objective Selection Genetic Algorithm (NMFS-GA), for selecting optimal feature subsets binary classification with labels. NMFS-GA offers unified framework that are both accurate and interpretable. evaluate on synthetic datasets noise, Breast Cancer dataset enriched features, real-world ADNI dementia conversion prediction. Our results indicate...

Multi-view datasets offer diverse forms of data that can enhance prediction models by providing complementary information. However, the use multi-view leads to an increase in high-dimensional data, which poses significant challenges for lead poor generalization. Therefore, relevant feature selection from is important as it not only addresses generalization but also enhances interpretability models. Despite success traditional methods, they have limitations leveraging intrinsic information...

It is well-established that brain size associated with intelligence. But the relationship between cortical morphometric measures and intelligence unclear. Studies have produced conflicting results or no significant relations such as thickness peri-cortical contrast. This discrepancy may be due to multicollinearity amongst independent variables in a multivariate regression analysis, failure fully account for some other way. Our study shows neither nor contrast reliably improves IQ prediction...

Multi-view datasets offer diverse forms of data that can enhance prediction models by providing complementary information. However, the use multi-view leads to an increase in high-dimensional data, which poses significant challenges for lead poor generalization. Therefore, relevant feature selection from is important as it not only addresses generalization but also enhances interpretability models. Despite success traditional methods, they have limitations leveraging intrinsic information...

We propose a four-layer fully-connected neural network (FNN) for predicting fluid intelligence scores from T1-weighted MR images the ABCD-challenge. In addition to volumes of brain structures, FNN uses cortical WM/GM contrast and thickness at 78 regions. These last two measurements were derived using surfaces produced by CIVET pipeline. The age gender subjects scanner manufacturer are also used as features learning algorithm. This yielded 283 provided with hidden layers 20 15 nodes. method...

Machine learning techniques typically applied to dementia forecasting lack in their capabilities jointly learn several tasks, handle time dependent heterogeneous data and missing values. In this paper, we propose a framework using the recently presented SSHIBA model for different tasks on longitudinal with The method uses Bayesian variational inference impute values combine information of views. This way, can data-views from time-points common latent space relations between each time-point...

The Alzheimer's Disease Assessment Scale-Cognitive subscale (ADAS-Cog) is a neuropsychological tool that has been designed to assess the severity of cognitive symptoms dementia. Personalized prediction changes in ADAS-Cog scores could help timing therapeutic interventions dementia and at-risk populations. In present work, we compared single multitask learning approaches predict based on T1-weighted anatomical magnetic resonance imaging (MRI). contrast most machine learning-based methods...