mlsanityData to 7 Oct 2026

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

arXiv 2407.00028 ↗First posted 14 May 2024q-bio.NC cs.LG stat.APcited by 26

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Abstract (arXiv)

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).

Key facts. Cited by 26 papers in the map (data through 7 Oct 2026). Builds on 11 of its 23 references that are in the map. First posted on arXiv 14 May 2024.

Builds on

11 of 23 references.

References · 11

  1. 2016XGBoost: A Scalable Tree Boosting Systemcited by 5,120
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  6. 2024Image Captioning in news report scenariocited by 11
  7. 2024Localization Through Particle Filter Powered Neural Network Estimated Monocular Camera Posescited by 11
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  9. 2024Utilizing Large Language Models for Information Extraction from Real Estate Transactionscited by 7
  10. 2024Research on emotionally intelligent dialogue generation based on automatic dialogue systemcited by 5
  11. 2024Leveraging Deep Learning and Xception Architecture for High-Accuracy MRI Classification in Alzheimer Diagnosiscited by 4

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Citing papers per year: 2024: 26.

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