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<p>Parkinson’s disease (PD) is a disorder involving progressive degeneration of the nervous system. Its clinical signs typically become noticeable only after substantial impairment has occurred in the substantia nigra, a brain region involved in motor control. Therefore, early and accurate prediction of PD based on molecular alterations is essential for proper diagnosis and improved patient outcomes. In this context, feature-level multi-omics integration has become a useful strategy because it combines molecular information from multiple biological levels and may support a broader understanding of disease progression. This study proposes a novel technique called Semi-supervised Ensemble Learning with Interval Type-2 Fuzzy-Rough Sets (SSEnIT2FRS) for PD prediction from multi-omics data, which leverages interval type-2 fuzzy-rough sets to achieve early and accurate prediction. The type-2 fuzzy-rough set enhances the handling of uncertainty and vagueness present in real-world biological datasets, while the ensemble approach improves predictive robustness by combining the decisions of multiple base classifiers. Furthermore, the inclusion of semi-supervised learning addresses the scarcity of labeled samples. The experimental findings indicate that the proposed method outperforms nine existing methods across all evaluation metrics, achieving the highest accuracy of 98.49%, with precision 0.9820, recall 0.9865, macro <InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(F_{1}\)</EquationSource> </InlineEquation>-score 0.9836, micro <InlineEquation ID="IEq2"> <EquationSource Format="TEX">\(F_{1}\)</EquationSource> </InlineEquation>-score 0.9842, kappa 0.9672, specificity 0.9953, and AUC of 0.9969 on the multi-omics dataset. Boxplots, paired <i>t</i>-test results, confidence interval analysis, and SHAP-based explanations further justify the superior performance, robustness, statistical significance, and interpretability of the proposed method. Therefore, the proposed method could be an effective computational technique for identifying PD at an early stage from multi-omics data.</p>

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Semi-supervised ensemble learning with interval type-2 fuzzy-rough sets for Parkinson’s disease prediction from multi-omics

  • 期刊论文

  • 发表日期：

    2026年7月11日

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  • Ambika Hazarika,
  • Ansuman Kumar,
  • Anindya Halder

摘要

Parkinson’s disease (PD) is a disorder involving progressive degeneration of the nervous system. Its clinical signs typically become noticeable only after substantial impairment has occurred in the substantia nigra, a brain region involved in motor control. Therefore, early and accurate prediction of PD based on molecular alterations is essential for proper diagnosis and improved patient outcomes. In this context, feature-level multi-omics integration has become a useful strategy because it combines molecular information from multiple biological levels and may support a broader understanding of disease progression. This study proposes a novel technique called Semi-supervised Ensemble Learning with Interval Type-2 Fuzzy-Rough Sets (SSEnIT2FRS) for PD prediction from multi-omics data, which leverages interval type-2 fuzzy-rough sets to achieve early and accurate prediction. The type-2 fuzzy-rough set enhances the handling of uncertainty and vagueness present in real-world biological datasets, while the ensemble approach improves predictive robustness by combining the decisions of multiple base classifiers. Furthermore, the inclusion of semi-supervised learning addresses the scarcity of labeled samples. The experimental findings indicate that the proposed method outperforms nine existing methods across all evaluation metrics, achieving the highest accuracy of 98.49%, with precision 0.9820, recall 0.9865, macro \(F_{1}\) -score 0.9836, micro \(F_{1}\) -score 0.9842, kappa 0.9672, specificity 0.9953, and AUC of 0.9969 on the multi-omics dataset. Boxplots, paired t-test results, confidence interval analysis, and SHAP-based explanations further justify the superior performance, robustness, statistical significance, and interpretability of the proposed method. Therefore, the proposed method could be an effective computational technique for identifying PD at an early stage from multi-omics data.