Development and nursing application of kidney disease prediction models based on machine learning.

IF 1.7 4区医学 Q3 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computer Methods in Biomechanics and Biomedical Engineering Pub Date : 2025-03-24 DOI:10.1080/10255842.2025.2479856

Yan Zhang, Hui Gao

引用次数: 0

Abstract

Kidney diseases complicate treatment prediction and progression. This study introduces a Metaheuristic Red Fox-Optimized Agile Support Vector Machine (MRFO-ASVM) for early detection and prognosis of kidney diseases. Nurses' involvement in data collection and analysis enhances model effectiveness. Pre-processing with Min-Max normalization and feature extraction using Principal Component Analysis (PCA) improves data quality. The MRFO-ASVM obtained enhanced parameter performance of the model including high accuracy (0.92), F1-score (0.67), sensitivity (0.89), precision (0.63), and ROC-AUC (0.99). Integrating this technology into nursing practice enhances early detection and personalized care, advancing patient-centred healthcare solutions.

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来源期刊

Computer Methods in Biomechanics and Biomedical Engineering 工程技术-工程：生物医学

CiteScore

4.10

自引率

6.20%

发文量

179

审稿时长

4-8 weeks

期刊介绍： The primary aims of Computer Methods in Biomechanics and Biomedical Engineering are to provide a means of communicating the advances being made in the areas of biomechanics and biomedical engineering and to stimulate interest in the continually emerging computer based technologies which are being applied in these multidisciplinary subjects. Computer Methods in Biomechanics and Biomedical Engineering will also provide a focus for the importance of integrating the disciplines of engineering with medical technology and clinical expertise. Such integration will have a major impact on health care in the future.