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Semi-supervised ensemble learning with interval type-2 fuzzy-rough sets for Parkinson's disease prediction from
Ambika Hazarika1, Ansuman Kumar2, Anindya Halder1
1Department of Computer Application, North-Eastern Hill University, Tura Campus, West Garo Hills, Meghalaya, 794002, India.
Summary
This study introduces a new computational method for early Parkinson's disease (PD) prediction using multi-omics data. The novel Semi-supervised Ensemble Learning with Interval Type-2 Fuzzy-Rough Sets (SSEnIT2FRS) achieved 98.49% accuracy, outperforming existing techniques.
Area of Science:
- Computational biology and bioinformatics
- Neuroscience and neurology
- Machine learning and artificial intelligence
Background:
- Parkinson's disease (PD) involves progressive neurodegeneration, with symptoms appearing after significant substantia nigra impairment.
- Early and accurate PD prediction is crucial for timely diagnosis and improved patient outcomes.
- Multi-omics data integration offers a comprehensive view of disease progression by combining molecular information from various biological levels.
Purpose of the Study:
- To develop a novel computational technique for early and accurate prediction of Parkinson's disease (PD) using multi-omics data.
- To leverage the strengths of semi-supervised learning, ensemble methods, and interval type-2 fuzzy-rough sets for enhanced predictive performance.
- To address the challenges of uncertainty and limited labeled samples in biological datasets for PD prediction.
Main Methods:
- Proposed a novel method: Semi-supervised Ensemble Learning with Interval Type-2 Fuzzy-Rough Sets (SSEnIT2FRS).
- Utilized interval type-2 fuzzy-rough sets to effectively handle uncertainty and vagueness in multi-omics data.
- Employed an ensemble approach for improved predictive robustness and semi-supervised learning to manage scarce labeled data.
Main Results:
- The SSEnIT2FRS method achieved a high accuracy of 98.49% on the multi-omics dataset.
- Demonstrated superior performance over nine existing methods across all evaluation metrics, including precision, recall, F1-score, and AUC.
- Statistical analyses (t-tests, confidence intervals) and SHAP explanations confirmed the method's robustness, significance, and interpretability.
Conclusions:
- The proposed SSEnIT2FRS method is a highly effective computational tool for early PD identification from multi-omics data.
- The integration of advanced fuzzy-rough set theory and ensemble learning significantly enhances prediction accuracy and reliability.
- This approach holds promise for improving early diagnosis and patient management in Parkinson's disease.
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