Harnessing XGBoost for Robust Biomarker Selection of Obsessive-Compulsive Disorder (OCD) from Adolescent Brain Cognitive Development (ABCD) data

• URL: http://arxiv.org/abs/2407.00028v1
• Date: Tue, 14 May 2024 23:43:34 GMT
• Title: Harnessing XGBoost for Robust Biomarker Selection of Obsessive-Compulsive Disorder (OCD) from Adolescent Brain Cognitive Development (ABCD) data
• Authors: Xinyu Shen, Qimin Zhang, Huili Zheng, Weiwei Qi,
• Abstract summary: This study evaluates the performance of various supervised machine learning models in analyzing highly correlated neural signaling data from the Adolescent Brain Cognitive Development (ABCD) Study. We simulated a dataset to mimic the correlation structures commonly found in neuroimaging data and evaluated logistic regression, elastic networks, random forests, and XGBoost on their ability to handle multicollinearity and accurately identify predictive features.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This study evaluates the performance of various supervised machine learning models in analyzing highly correlated neural signaling data from the Adolescent Brain Cognitive Development (ABCD) Study, with a focus on predicting obsessive-compulsive disorder scales. We simulated a dataset to mimic the correlation structures commonly found in imaging data and evaluated logistic regression, elastic networks, random forests, and XGBoost on their ability to handle multicollinearity and accurately identify predictive features. Our study aims to guide the selection of appropriate machine learning methods for processing neuroimaging data, highlighting models that best capture underlying signals in high feature correlations and prioritize clinically relevant features associated with Obsessive-Compulsive Disorder (OCD).

Related papers

• Exploring General Intelligence via Gated Graph Transformer in Functional Connectivity Studies [39.82681427764513]
  Gated Graph Transformer (GGT) framework designed to predict cognitive metrics based on Functional Connectivity (FC) Empirical validation on the Philadelphia Neurodevelopmental Cohort (PNC) underscores the superior predictive prowess of our model.
  arXiv  Detail & Related papers  (2024-01-18T19:28:26Z)
• MBrain: A Multi-channel Self-Supervised Learning Framework for Brain Signals [7.682832730967219]
  We study the self-supervised learning framework for brain signals that can be applied to pre-train either SEEG or EEG data. Inspired by this, we propose MBrain to learn implicit spatial and temporal correlations between different channels. Our model outperforms several state-of-the-art time series SSL and unsupervised models, and has the ability to be deployed to clinical practice.
  arXiv  Detail & Related papers  (2023-06-15T09:14:26Z)
• NeuroGraph: Benchmarks for Graph Machine Learning in Brain Connectomics [10.294767093317404]
  We introduce NeuroGraph, a collection of graph-based neuroimaging datasets. We demonstrate its utility for predicting multiple categories of behavioral and cognitive traits.
  arXiv  Detail & Related papers  (2023-06-09T19:10:16Z)
• A Novel Supervised Contrastive Regression Framework for Prediction of Neurocognitive Measures Using Multi-Site Harmonized Diffusion MRI Tractography [13.80649748804573]
  Supervised Contrastive Regression (SCR) is a simple yet effective method that allows full supervision for contrastive learning in regression tasks. SCR performs supervised contrastive representation learning by using the absolute difference between continuous regression labels. SCR improves the accuracy of neurocognitive score prediction compared to other state-of-the-art methods.
  arXiv  Detail & Related papers  (2022-10-13T23:24:12Z)
• Classification of ADHD Patients Using Kernel Hierarchical Extreme Learning Machine [3.39487428163997]
  We utilize the dynamics of brain functional connectivity to model features from medical imaging data. Our results achieved superior classification rates compared to the state-of-the-art models.
  arXiv  Detail & Related papers  (2022-06-28T05:17:54Z)
• Overcoming the Domain Gap in Contrastive Learning of Neural Action Representations [60.47807856873544]
  A fundamental goal in neuroscience is to understand the relationship between neural activity and behavior. We generated a new multimodal dataset consisting of the spontaneous behaviors generated by fruit flies. This dataset and our new set of augmentations promise to accelerate the application of self-supervised learning methods in neuroscience.
  arXiv  Detail & Related papers  (2021-11-29T15:27:51Z)
• Learning Neural Causal Models with Active Interventions [83.44636110899742]
  We introduce an active intervention-targeting mechanism which enables a quick identification of the underlying causal structure of the data-generating process. Our method significantly reduces the required number of interactions compared with random intervention targeting. We demonstrate superior performance on multiple benchmarks from simulated to real-world data.
  arXiv  Detail & Related papers  (2021-09-06T13:10:37Z)
• G-MIND: An End-to-End Multimodal Imaging-Genetics Framework for Biomarker Identification and Disease Classification [49.53651166356737]
  We propose a novel deep neural network architecture to integrate imaging and genetics data, as guided by diagnosis, that provides interpretable biomarkers. We have evaluated our model on a population study of schizophrenia that includes two functional MRI (fMRI) paradigms and Single Nucleotide Polymorphism (SNP) data.
  arXiv  Detail & Related papers  (2021-01-27T19:28:04Z)
• Neuro-symbolic Neurodegenerative Disease Modeling as Probabilistic Programmed Deep Kernels [93.58854458951431]
  We present a probabilistic programmed deep kernel learning approach to personalized, predictive modeling of neurodegenerative diseases. Our analysis considers a spectrum of neural and symbolic machine learning approaches. We run evaluations on the problem of Alzheimer's disease prediction, yielding results that surpass deep learning.
  arXiv  Detail & Related papers  (2020-09-16T15:16:03Z)
• Select-ProtoNet: Learning to Select for Few-Shot Disease Subtype Prediction [55.94378672172967]
  We focus on few-shot disease subtype prediction problem, identifying subgroups of similar patients. We introduce meta learning techniques to develop a new model, which can extract the common experience or knowledge from interrelated clinical tasks. Our new model is built upon a carefully designed meta-learner, called Prototypical Network, that is a simple yet effective meta learning machine for few-shot image classification.
  arXiv  Detail & Related papers  (2020-09-02T02:50:30Z)
• Dynamic Graph Correlation Learning for Disease Diagnosis with Incomplete Labels [66.57101219176275]
  Disease diagnosis on chest X-ray images is a challenging multi-label classification task. We propose a Disease Diagnosis Graph Convolutional Network (DD-GCN) that presents a novel view of investigating the inter-dependency among different diseases. Our method is the first to build a graph over the feature maps with a dynamic adjacency matrix for correlation learning.
  arXiv  Detail & Related papers  (2020-02-26T17:10:48Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.