BDPM: A Machine Learning-Based Feature Extractor for Parkinson's Disease Classification via Gut Microbiota Analysis

2025-09-09

AI Summary

High clinical misdiagnosis rates persist in Parkinson’s disease (PD), and existing deep learning models leveraging gut microbiome data predominantly rely on single classifiers, neglecting ecological interdependencies among microbial taxa and longitudinal dynamics, while suffering from limited feature interpretability. To address these limitations, we propose RFRE—a biologically informed feature selection framework integrating random forests with recursive feature elimination, explicitly incorporating microbial ecological priors to enhance biological interpretability. Furthermore, we design a spatiotemporal-aware hybrid classifier that jointly models cross-sectional compositional differences and longitudinal temporal evolution of microbial abundances. Evaluated on a cohort of 39 PD patient–healthy spouse pairs, our approach achieves significantly improved classification accuracy and robustly identifies discriminative microbial taxa. This work establishes a novel paradigm for early, interpretable, microbiome-based PD diagnosis.

Technology Category

• Machine Learning: Dimensionality Reduction/Feature Selection
• Knowledge Representation and Reasoning: Diagnosis and Abductive Reasoning
• Reasoning under Uncertainty: Relational Probabilistic Models

Application Category

• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization
• Web Mining and Content Analysis: Large pretrained models with web data
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Background: Parkinson's disease remains a major neurodegenerative disorder with high misdiagnosis rates, primarily due to reliance on clinical rating scales. Recent studies have demonstrated a strong association between gut microbiota and Parkinson's disease, suggesting that microbial composition may serve as a promising biomarker. Although deep learning models based ongut microbiota show potential for early prediction, most approaches rely on single classifiers and often overlook inter-strain correlations or temporal dynamics. Therefore, there is an urgent need for more robust feature extraction methods tailored to microbiome data. Methods: We proposed BDPM (A Machine Learning-Based Feature Extractor for Parkinson's Disease Classification via Gut Microbiota Analysis). First, we collected gut microbiota profiles from 39 Parkinson's patients and their healthy spouses to identify differentially abundant taxa. Second, we developed an innovative feature selection framework named RFRE (Random Forest combined with Recursive Feature Elimination), integrating ecological knowledge to enhance biological interpretability. Finally, we designed a hybrid classification model to capture temporal and spatial patterns in microbiome data.

Problem

• Develops feature extraction for Parkinson's disease classification
• Addresses misdiagnosis through gut microbiota analysis
• Captures temporal and spatial microbiome patterns

Innovation

• Machine learning-based feature extractor for Parkinson's
• RFRE feature selection with ecological knowledge integration
• Hybrid classification model capturing temporal-spatial microbiome patterns