A method for reducing medical test indexes based on the combination of rough set positive domain and information entropy

By combining the rough set positive domain and information entropy methods, the comprehensive importance of medical test indicators is calculated, which solves the problem of redundant attributes in high-dimensional data and achieves more accurate simplification and improved diagnostic efficiency.

CN120408127BActive Publication Date: 2025-09-09NANJING UNIV OF INFORMATION SCI & TECH
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Patent Information

Application Number
CN202510919382.6
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2025-07-04
Publication Date
2025-09-09
Estimated Expiration
2045-07-04

AI Technical Summary

Technical Problem

Existing medical test index data processing methods are unable to effectively balance deterministic and non-deterministic elements in high-dimensional data, resulting in increased redundant attributes and reduced model generalization ability and diagnostic efficiency.

Method used

A method based on the combination of rough set positive domain and information entropy is adopted to calculate the comprehensive importance of medical test indicators through weighted average, and iterative simplification is performed to reasonably balance deterministic and non-deterministic elements to generate a more accurate simplified set.

Benefits of technology

It improves the diagnostic accuracy and efficiency of medical tests, reduces unnecessary test items, reduces medical costs, and enhances clinical interpretability.

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Abstract

The present invention discloses a medical test index simplification method based on the combination of rough set positive domain and information entropy, which is suitable for high-dimensional data attribute simplification and knowledge discovery in scenarios such as medical data analysis. By integrating the two perspectives of rough set theory and information theory methods, a new simplification strategy is proposed, which obtains the comprehensive importance of attributes by weighted average, and its weight is determined by the ratio of the positive domain to the boundary domain. It performs iterative simplification according to the comprehensive importance, reasonably takes into account the deterministic and non-deterministic elements, solves the feasibility problem of attribute simplification in large-scale fuzzy information decision-making systems, and obtains a more accurate simplified set. It is convenient to focus on key diagnostic indicators, improve diagnostic efficiency and accuracy, enhance clinical interpretability, reduce unnecessary test items, and play a role in optimizing data, improving diagnostic efficiency, and saving medical costs.
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