Diabetes feature selection method and system based on optimized dogvessel algorithm

By introducing a Logistic chaotic mapping mechanism and an adaptive mechanism to optimize the tunic algorithm, the problems of local optima and high computational complexity in feature selection are solved, achieving efficient and stable feature selection and improving the accuracy of the diabetes diagnostic model.

CN121617652APending Publication Date: 2026-03-06SHIHEZI UNIVERSITY
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Patent Information

Application Number
CN202511796040.6
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-12-02
Publication Date
2026-03-06

AI Technical Summary

Technical Problem

Existing feature selection methods are prone to getting trapped in local optima in high-dimensional feature spaces, resulting in low search efficiency, unstable results, and high computational complexity, making it difficult to meet the needs of practical applications.

Method used

A Logistic chaotic mapping mechanism is introduced for initializing the tunic algorithm. The leader position is calculated by combining adaptive weights and preset perturbation terms, and the follower position is calculated by preset following factors and perturbation terms. Feature selection is optimized by binarization mapping and fitness calculation.

Benefits of technology

It improves the accuracy, stability, and repeatability of feature selection, enhances search efficiency, reduces redundant features, and improves the accuracy of diabetes diagnostic models.

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Abstract

The invention discloses a diabetes feature selection method and system based on an optimized doliolaria algorithm. The method comprises the following steps: initializing an obtained high-dimensional diabetes medical feature data set for diagnosing diabetes by adopting a Logistic chaotic mapping mechanism to obtain a first-generation candidate diabetes medical feature data group; and in each iteration, respectively introducing a self-adaptive mechanism and a leader position updating formula and a follower position updating formula of a preset disturbance item, updating leader feature data and follower feature data in the current candidate diabetes medical feature data group, and obtaining a next-generation candidate diabetes medical feature data group. And repeating the process until a preset number of iterations is reached, and finally determining the leader feature data in the last-generation candidate diabetes medical feature data group as the target diabetes medical feature data. According to the application, by introducing chaotic mapping and an adaptive mechanism, the efficiency and precision of screening key features of diabetes mellitus by the doliolaria algorithm can be improved.
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