基于空间贝叶斯神经网络的心脏造影胎儿健康分类分析。

IF 1.7 4区医学 Q3 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computer Methods in Biomechanics and Biomedical Engineering Pub Date : 2025-04-10 DOI:10.1080/10255842.2025.2478293

P Solainayagi, G Sivagaminathan, Sabenabanu Abdulkadhar, A Gnana Soundari, K Krishnakumar

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引用次数: 0

摘要

妊娠并发症需要早期发现，但传统的心脏造影（CTG）分析是劳动密集型的，容易出错。本文介绍了利用小猫鼬优化器优化的空间贝叶斯神经网络（cda - fhc - sbn - dmo）对胎儿健康分类的心脏造影数据分析。该过程包括收集CTG数据，使用洪堡鱿鱼优化算法（HSOA）优化特征选择，并使用空间贝叶斯神经网络（SBNN）对胎儿健康进行分类。使用侏儒猫鹅优化器（DMO）对SBNN进行优化。CDA-FHC-SBNN-DMO方法在Python中实现，优于现有方法，准确率提高了20.89%，31.45%和28.32%，精度和召回率显著提高。

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Cardiotocography data analysis for foetal health classification using Spatial Bayesian Neural Network Optimized with Dwarf Mongoose Optimizer.

Pregnancy complications require early detection, but traditional Cardiotocography (CTG) analysis is labor-intensive and error-prone. This manuscript presents Cardiotocography Data Analysis for Foetal Health Classification using Spatial Bayesian Neural Network Optimized with Dwarf Mongoose Optimizer (CDA-FHC-SBNN-DMO). The process involves collecting CTG data, optimizing feature selection with Humboldt Squid Optimization Algorithm (HSOA) and classification using Spatial Bayesian Neural Network (SBNN) to categorize foetal health. Dwarf Mongoose Optimizer (DMO) is used to optimize SBNN. The CDA-FHC-SBNN-DMO method was implemented in Python, outperforms existing methods, achieving improvements of 20.89%, 31.45%, and 28.32% in accuracy, and significant increases in precision and recall.

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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.