Construction and application method and device of chronic kidney disease patient vulnerability risk prediction model

By constructing a vulnerability risk prediction model for patients with chronic kidney disease, and using machine learning models and recursive feature elimination to identify key features, this approach addresses the lack of vulnerability risk assessment for patients with chronic kidney disease in existing technologies, achieving efficient risk prediction and interpretability.

CN121393885APending Publication Date: 2026-01-23GUIZHOU PROVINCIAL PEOPLES HOSPITAL
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Patent Information

Application Number
CN202511557633.7
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-10-29
Publication Date
2026-01-23

AI Technical Summary

Technical Problem

There is currently no vulnerability risk prediction model for patients with chronic kidney disease, making it impossible to accurately assess their risk.

Method used

A vulnerability risk prediction model for chronic kidney disease patients based on a publicly available sample set was constructed. By acquiring the basic characteristics of the subjects, a machine learning model was trained and a recursive feature elimination method was applied to identify key features, and a target machine learning model was established for prediction.

Benefits of technology

It enables accurate prediction of vulnerability risk in patients with chronic kidney disease, improves the performance and interpretability of the prediction model, and provides clinical application value.

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Abstract

The invention discloses a construction and application method and device of a chronic kidney disease patient vulnerability risk prediction model, and relates to the technical field of data processing, and the construction method comprises the steps: obtaining a public sample set; the public sample set comprises basic characteristics of a plurality of subjects and corresponding fragile state results; the subjects comprise chronic kidney disease patients; training a plurality of machine learning models based on the public sample set, determining a machine learning model with optimal model performance, and obtaining an optimal machine learning model; based on the optimal machine learning model, determining key features in the basic features of the subject by applying a recursive feature elimination method; retraining the optimal machine learning model based on the key features and the corresponding fragile state results to obtain a target machine learning model; the target machine learning model is used for predicting the vulnerability risk of the chronic kidney disease patient. According to the invention, accurate prediction of the vulnerability risk of the chronic kidney disease patient is realized.
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