Impact of Data Patterns on Biotype identification Using Machine Learning

• URL: http://arxiv.org/abs/2503.12066v1
• Date: Sat, 15 Mar 2025 09:44:00 GMT
• Title: Impact of Data Patterns on Biotype identification Using Machine Learning
• Authors: Yuetong Yu, Ruiyang Ge, Ilker Hacihaliloglu, Alexander Rauscher, Roger Tam, Sophia Frangou,
• Abstract summary: This study investigates the contribution of data patterns on algorithm performance by leveraging synthetic brain morphometry data as an exemplar.<n>SuStaIn failed to process datasets with more than 17 variables, highlighting computational inefficiencies.<n>SmileGAN and SurrealGAN outperformed other algorithms in identifying variable-based disease patterns, but these patterns were not able to provide individual-level classification.
• Score: 38.321248253111776
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Background: Patient stratification in brain disorders remains a significant challenge, despite advances in machine learning and multimodal neuroimaging. Automated machine learning algorithms have been widely applied for identifying patient subtypes (biotypes), but results have been inconsistent across studies. These inconsistencies are often attributed to algorithmic limitations, yet an overlooked factor may be the statistical properties of the input data. This study investigates the contribution of data patterns on algorithm performance by leveraging synthetic brain morphometry data as an exemplar. Methods: Four widely used algorithms-SuStaIn, HYDRA, SmileGAN, and SurrealGAN were evaluated using multiple synthetic pseudo-patient datasets designed to include varying numbers and sizes of clusters and degrees of complexity of morphometric changes. Ground truth, representing predefined clusters, allowed for the evaluation of performance accuracy across algorithms and datasets. Results: SuStaIn failed to process datasets with more than 17 variables, highlighting computational inefficiencies. HYDRA was able to perform individual-level classification in multiple datasets with no clear pattern explaining failures. SmileGAN and SurrealGAN outperformed other algorithms in identifying variable-based disease patterns, but these patterns were not able to provide individual-level classification. Conclusions: Dataset characteristics significantly influence algorithm performance, often more than algorithmic design. The findings emphasize the need for rigorous validation using synthetic data before real-world application and highlight the limitations of current clustering approaches in capturing the heterogeneity of brain disorders. These insights extend beyond neuroimaging and have implications for machine learning applications in biomedical research.

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