Dataset Properties Shape the Success of Neuroimaging-Based Patient Stratification: A Benchmarking Analysis Across Clustering Algorithms

• URL: http://arxiv.org/abs/2503.12066v2
• Date: Tue, 10 Jun 2025 22:00:59 GMT
• Title: Dataset Properties Shape the Success of Neuroimaging-Based Patient Stratification: A Benchmarking Analysis Across Clustering Algorithms
• Authors: Yuetong Yu, Ruiyang Ge, Ilker Hacihaliloglu, Alexander Rauscher, Roger Tam, Sophia Frangou,
• Abstract summary: We evaluated 4 widely used stratification algorithms, HYDRA, SuStaIn, SmileGAN, and SurrealGAN, on a suite of synthetic brain-morphometry cohorts.<n>Across 122 synthetic scenarios, data complexity consistently outweighed algorithm choice in predicting stratification success.<n>Well-separated clusters yielded high accuracy for all methods, whereas overlapping, unequal-sized, or subtle effects reduced accuracy by up to 50%.
• Score: 38.321248253111776
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Background: Data driven stratification of patients into biologically informed subtypes holds promise for precision neuropsychiatry, yet neuroimaging-based clustering methods often fail to generalize across cohorts. While algorithmic innovations have focused on model complexity, the role of underlying dataset characteristics remains underexplored. We hypothesized that cluster separation, size imbalance, noise, and the direction and magnitude of disease-related effects in the input data critically determine both within-algorithm accuracy and reproducibility. Methods: We evaluated 4 widely used stratification algorithms, HYDRA, SuStaIn, SmileGAN, and SurrealGAN, on a suite of synthetic brain-morphometry cohorts derived from the Human Connectome Project Young Adult dataset. Three global transformation patterns were applied to 600 pseudo-patients against 508 controls, followed by 4 within-dataset variations varying cluster count (k=2-6), overlap, and effect magnitude. Algorithm performance was quantified by accuracy in recovering the known ground-truth clusters. Results: Across 122 synthetic scenarios, data complexity consistently outweighed algorithm choice in predicting stratification success. Well-separated clusters yielded high accuracy for all methods, whereas overlapping, unequal-sized, or subtle effects reduced accuracy by up to 50%. SuStaIn could not scale beyond 17 features, HYDRA's accuracy varied unpredictably with data heterogeneity. SmileGAN and SurrealGAN maintained robust pattern detection but did not assign discrete cluster labels to individuals. Conclusions: The study results demonstrate the impact of statistical properties of input data across algorithms and highlight the need for using realistic dataset distributions when new algorithms are being developed and suggest greater focus on data-centric strategies that actively shape and standardize the input distributions.

Related papers

• Advancing Tabular Stroke Modelling Through a Novel Hybrid Architecture and Feature-Selection Synergy [0.9999629695552196]
  The present work develops and validates a data-driven and interpretable machine-learning framework designed to predict strokes.<n>Ten routinely gathered demographic, lifestyle, and clinical variables were sourced from a public cohort of 4,981 records.<n>The proposed model achieved an accuracy rate of 97.2% and an F1-score of 97.15%, indicating a significant enhancement compared to the leading individual model.
  arXiv  Detail & Related papers  (2025-05-18T21:46:45Z)
• Precision Adaptive Imputation Network : An Unified Technique for Mixed Datasets [0.0]
  This study introduces the Precision Adaptive Imputation Network (PAIN), a novel algorithm designed to enhance data reconstruction.<n>PAIN employs a tri-step process that integrates statistical methods, random forests, and autoencoders, ensuring balanced accuracy and efficiency in imputation.<n>The findings highlight PAIN's superior ability to preserve data distributions and maintain analytical integrity, particularly in complex scenarios where missingness is not completely at random.
  arXiv  Detail & Related papers  (2025-01-18T06:22:27Z)
• Learning to refine domain knowledge for biological network inference [2.209921757303168]
  Perturbation experiments allow biologists to discover causal relationships between variables of interest. The sparsity and high dimensionality of these data pose significant challenges for causal structure learning algorithms. We propose an amortized algorithm for refining domain knowledge, based on data observations.
  arXiv  Detail & Related papers  (2024-10-18T12:53:23Z)
• Artificial Data Point Generation in Clustered Latent Space for Small Medical Datasets [4.542616945567623]
  This paper introduces a novel method, Artificial Data Point Generation in Clustered Latent Space (AGCL) AGCL is designed to enhance classification performance on small medical datasets through synthetic data generation. It was applied to Parkinson's disease screening, utilizing facial expression data.
  arXiv  Detail & Related papers  (2024-09-26T09:51:08Z)
• An Autoencoder and Generative Adversarial Networks Approach for Multi-Omics Data Imbalanced Class Handling and Classification [2.2940141855172036]
  In molecular biology, there has been an explosion of data generated from multi-omics sequencing. Traditional statistical methods face challenging tasks when dealing with such high dimensional data. This study, focused on tackling these challenges in a neural network that incorporates autoencoder to extract latent space of the features.
  arXiv  Detail & Related papers  (2024-05-16T01:45:55Z)
• Few-shot learning for COVID-19 Chest X-Ray Classification with Imbalanced Data: An Inter vs. Intra Domain Study [49.5374512525016]
  Medical image datasets are essential for training models used in computer-aided diagnosis, treatment planning, and medical research. Some challenges are associated with these datasets, including variability in data distribution, data scarcity, and transfer learning issues when using models pre-trained from generic images. We propose a methodology based on Siamese neural networks in which a series of techniques are integrated to mitigate the effects of data scarcity and distribution imbalance.
  arXiv  Detail & Related papers  (2024-01-18T16:59:27Z)
• Amplifying Pathological Detection in EEG Signaling Pathways through Cross-Dataset Transfer Learning [10.212217551908525]
  We study the effectiveness of data and model scaling and cross-dataset knowledge transfer in a real-world pathology classification task. We identify the challenges of possible negative transfer and emphasize the significance of some key components. Our findings indicate a small and generic model (e.g. ShallowNet) performs well on a single dataset, however, a larger model (e.g. TCN) performs better on transfer and learning from a larger and diverse dataset.
  arXiv  Detail & Related papers  (2023-09-19T20:09:15Z)
• The effect of data augmentation and 3D-CNN depth on Alzheimer's Disease detection [51.697248252191265]
  This work summarizes and strictly observes best practices regarding data handling, experimental design, and model evaluation. We focus on Alzheimer's Disease (AD) detection, which serves as a paradigmatic example of challenging problem in healthcare. Within this framework, we train predictive 15 models, considering three different data augmentation strategies and five distinct 3D CNN architectures.
  arXiv  Detail & Related papers  (2023-09-13T10:40:41Z)
• Machine Learning Based Missing Values Imputation in Categorical Datasets [2.5611256859404983]
  This research looked into the use of machine learning algorithms to fill in the gaps in categorical datasets. The emphasis was on ensemble models constructed using the Error Correction Output Codes framework. Deep learning for missing data imputation has obstacles despite these encouraging results, including the requirement for large amounts of labeled data.
  arXiv  Detail & Related papers  (2023-06-10T03:29:48Z)
• Learning to Bound Counterfactual Inference in Structural Causal Models from Observational and Randomised Data [64.96984404868411]
  We derive a likelihood characterisation for the overall data that leads us to extend a previous EM-based algorithm. The new algorithm learns to approximate the (unidentifiability) region of model parameters from such mixed data sources. It delivers interval approximations to counterfactual results, which collapse to points in the identifiable case.
  arXiv  Detail & Related papers  (2022-12-06T12:42:11Z)
• RandomSCM: interpretable ensembles of sparse classifiers tailored for omics data [59.4141628321618]
  We propose an ensemble learning algorithm based on conjunctions or disjunctions of decision rules. The interpretability of the models makes them useful for biomarker discovery and patterns discovery in high dimensional data.
  arXiv  Detail & Related papers  (2022-08-11T13:55:04Z)
• Tk-merge: Computationally Efficient Robust Clustering Under General Assumptions [0.0]
  We present a two-step hybrid robust clustering algorithm based on trimmed k-means and hierarchical agglomeration. We also present natural generalizations of the approach as well as an adaptive procedure to estimate the amount of contamination in a data-driven fashion.
  arXiv  Detail & Related papers  (2022-01-17T13:05:05Z)
• MIRACLE: Causally-Aware Imputation via Learning Missing Data Mechanisms [82.90843777097606]
  We propose a causally-aware imputation algorithm (MIRACLE) for missing data. MIRACLE iteratively refines the imputation of a baseline by simultaneously modeling the missingness generating mechanism. We conduct extensive experiments on synthetic and a variety of publicly available datasets to show that MIRACLE is able to consistently improve imputation.
  arXiv  Detail & Related papers  (2021-11-04T22:38:18Z)
• Convolutional generative adversarial imputation networks for spatio-temporal missing data in storm surge simulations [86.5302150777089]
  Generative Adversarial Imputation Nets (GANs) and GAN-based techniques have attracted attention as unsupervised machine learning methods. We name our proposed method as Con Conval Generative Adversarial Imputation Nets (Conv-GAIN)
  arXiv  Detail & Related papers  (2021-11-03T03:50:48Z)
• 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)
• Bootstrapping Your Own Positive Sample: Contrastive Learning With Electronic Health Record Data [62.29031007761901]
  This paper proposes a novel contrastive regularized clinical classification model. We introduce two unique positive sampling strategies specifically tailored for EHR data. Our framework yields highly competitive experimental results in predicting the mortality risk on real-world COVID-19 EHR data.
  arXiv  Detail & Related papers  (2021-04-07T06:02:04Z)
• Object-Attribute Biclustering for Elimination of Missing Genotypes in Ischemic Stroke Genome-Wide Data [2.0236506875465863]
  Missing genotypes can affect the efficacy of machine learning approaches to identify the risk genetic variants of common diseases and traits. The problem occurs when genotypic data are collected from different experiments with different DNA microarrays, each being characterised by its pattern of uncalled (missing) genotypes. We use well-developed notions of object-attribute biclusters and formal concepts that correspond to dense subrelations in the binary relation.
  arXiv  Detail & Related papers  (2020-10-22T12:27:43Z)
• 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)

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.