Synergistic feature selection and distributed classification framework for high-dimensional medical data analysis.

Area of Science:

• Medical Data Analysis
• Machine Learning
• Computational Biology

Background:

• Medical datasets are large and complex, leading to computational challenges, memory limitations, and reduced classification accuracy.
• Effective feature selection and classification are crucial for accurate medical data interpretation and decision-making.

Purpose of the Study:

• To introduce an integrated algorithm, Synergistic Kruskal-RFE Selector and Distributed Multi-Kernel Classification Framework (SKR-DMKCF), to address limitations in medical data analysis.
• To improve dimensionality reduction, feature preservation, and classification performance in complex medical datasets.

Main Methods:

• Developed the Synergistic Kruskal-RFE Selector and Distributed Multi-Kernel Classification Framework (SKR-DMKCF).
• Utilized recursive feature elimination and multi-kernel classification in a distributed environment.
• Evaluated the algorithm on four diverse medical datasets.

Main Results:

• Achieved an average feature reduction ratio of 89% with SKR-DMKCF.
• Obtained an average classification accuracy of 85.3%, precision of 81.5%, and recall of 84.7%.
• Demonstrated a 25% reduction in memory usage and significant speed-up compared to existing methods.

Conclusions:

• SKR-DMKCF effectively reduces dimensionality while preserving critical data characteristics.
• The proposed framework offers superior classification accuracy and computational efficiency, ensuring scalability for resource-limited environments.