Design of application-oriented disease diagnosis model using a meta-heuristic algorithm.

IF 1.4 4区医学 Q4 ENGINEERING, BIOMEDICAL

Technology and Health Care Pub Date : 2024-07-13 DOI:10.3233/THC-231755

Zuoshan Wang, Shilin Wang, Manya Wang, Yan Sun

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引用次数: 0

Abstract

Background: Healthcare is crucial to patient care because it provides vital services for maintaining and restoring health. As healthcare technology evolves, cutting-edge tools facilitate faster diagnosis and more effective patient treatment. In the present age of pandemics, the Internet of Things (IoT) offers a potential solution to the problem of patient safety monitoring by creating a massive quantity of data about the patient through the linked devices around them and then analyzing it to estimate the patient's current status. Utilizing the IoT-based meta-heuristic algorithm allows patients to be remotely monitored, resulting in timely diagnosis and improved care. Meta-heuristic algorithms are successful, resilient, and effective in solving real-world enhancement, clustering, predicting, and grouping. Healthcare organizations need an efficient method for dealing with big data since the prevalence of such data makes it challenging to analyze for diagnosis. The current techniques used in medical diagnostics have limitations due to imbalanced data and the overfitting issue.

Objective: This study introduces the particle swarm optimization and convolutional neural network to be used as a meta-heuristic optimization method for extensive data analysis in the IoT to monitor patients' health conditions.

Method: Particle Swarm Optimization is used to optimize the data used in the study. Information for a diabetes diagnosis model that includes cardiac risk forecasting is collected. Particle Swarm Optimization and Convolutional Neural Networks (PSO-CNN) results effectively make illness predictions. Support Vector Machine has been used to predict the possibility of a heart attack based on the classification of the collected data into projected abnormal and normal ranges for diabetes.

Results: The results of the simulations reveal that the PSO-CNN model used to predict diabetic disease increased in accuracy by 92.6%, precision by 92.5%, recall by 93.2%, F1-score by 94.2%, and quantization error by 4.1%.

Conclusion: The suggested approach could be applied to identify cancer cells.

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使用元启发式算法设计面向应用的疾病诊断模型。

背景：医疗保健对病人护理至关重要，因为它提供了维护和恢复健康的重要服务。随着医疗保健技术的发展，尖端工具有助于更快地诊断和更有效地治疗病人。在当前流行病频发的时代，物联网（IoT）为患者安全监控问题提供了一个潜在的解决方案，即通过患者身边的联网设备创建大量有关患者的数据，然后通过分析这些数据来估计患者当前的状态。利用基于物联网的元启发式算法，可以对患者进行远程监控，从而及时诊断和改善护理。元启发式算法在解决现实世界中的增强、聚类、预测和分组等问题上是成功的、有弹性的和有效的。医疗机构需要一种高效的方法来处理大数据，因为这些数据的普遍性使得分析诊断具有挑战性。由于数据不平衡和过拟合问题，目前用于医疗诊断的技术存在局限性：本研究介绍了粒子群优化和卷积神经网络，将其作为一种元启发式优化方法，用于物联网中的大量数据分析，以监测患者的健康状况：方法：采用粒子群优化法对研究中使用的数据进行优化。收集糖尿病诊断模型的信息，其中包括心脏风险预测。粒子群优化和卷积神经网络（PSO-CNN）的结果有效地预测了疾病。支持向量机被用于根据收集到的数据分类预测心脏病发作的可能性，并将其分为糖尿病的预测异常和正常范围：模拟结果显示，用于预测糖尿病疾病的 PSO-CNN 模型的准确率提高了 92.6%，精确度提高了 92.5%，召回率提高了 93.2%，F1 分数提高了 94.2%，量化误差降低了 4.1%：结论：建议的方法可用于识别癌细胞。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Technology and Health Care HEALTH CARE SCIENCES & SERVICES-ENGINEERING, BIOMEDICAL

CiteScore

2.10

自引率

6.20%

发文量

282

审稿时长

>12 weeks

期刊介绍： Technology and Health Care is intended to serve as a forum for the presentation of original articles and technical notes, observing rigorous scientific standards. Furthermore, upon invitation, reviews, tutorials, discussion papers and minisymposia are featured. The main focus of THC is related to the overlapping areas of engineering and medicine. The following types of contributions are considered: 1.Original articles: New concepts, procedures and devices associated with the use of technology in medical research and clinical practice are presented to a readership with a widespread background in engineering and/or medicine. In particular, the clinical benefit deriving from the application of engineering methods and devices in clinical medicine should be demonstrated. Typically, full length original contributions have a length of 4000 words, thereby taking duly into account figures and tables. 2.Technical Notes and Short Communications: Technical Notes relate to novel technical developments with relevance for clinical medicine. In Short Communications, clinical applications are shortly described. 3.Both Technical Notes and Short Communications typically have a length of 1500 words. Reviews and Tutorials (upon invitation only): Tutorial and educational articles for persons with a primarily medical background on principles of engineering with particular significance for biomedical applications and vice versa are presented. The Editorial Board is responsible for the selection of topics. 4.Minisymposia (upon invitation only): Under the leadership of a Special Editor, controversial or important issues relating to health care are highlighted and discussed by various authors. 5.Letters to the Editors: Discussions or short statements (not indexed).