分析早期慢性肾病的智能诊断系统在临床中的应用

IF 2.4 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
N. I. Md. Ashafuddula, Bayezid Islam, Rafiqul Islam
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引用次数: 0

摘要

慢性肾脏病(CKD)是一种渐进性疾病,其特点是肾功能逐渐恶化,如果不及时诊断和治疗,有可能导致肾衰竭。机器学习(ML)算法在疾病诊断方面已显示出显著的前景,但在医疗保健领域,临床数据带来了挑战:缺失值、噪声输入和冗余特征,影响了早期 CKD 预测。因此,本研究提出了一种新颖的全自动机器学习方法,通过结合特征选择(FS)和特征空间缩小(FSR)技术来解决这些复杂问题,从而大大提高了模型的性能。在预处理过程中还采用了数据平衡技术,以解决临床中经常遇到的数据不平衡问题。最后,为了实现可靠的 CKD 分类,我们鼓励使用基于集合特征的分类器。我们的方法在多个数据集上进行了严格的验证和评估,并在从孟加拉国患者收集的真实世界治疗数据上评估了该策略的临床相关性。研究结果表明,在预测未见的 CKD 治疗数据(尤其是早期病例)时,自适应提升、逻辑回归和被动攻击型 ML 分类器的准确率高达 96.48%,占据主导地位。此外,FSR 技术还能有效缩短预测时间。所提模型的出色表现表明,通过结合 FS 和 FSR 技术,该模型能有效处理复杂的医疗慢性肾病数据。这凸显了该模型作为医生的计算机辅助诊断工具的潜力,可实现早期干预并改善患者预后。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An Intelligent Diagnostic System to Analyze Early-Stage Chronic Kidney Disease for Clinical Application
Chronic kidney disease (CKD) is a progressive condition characterized by the gradual deterioration of kidney functions, potentially leading to kidney failure if not promptly diagnosed and treated. Machine learning (ML) algorithms have shown significant promise in disease diagnosis, but in healthcare, clinical data pose challenges: missing values, noisy inputs, and redundant features, affecting early-stage CKD prediction. Thus, this study presents a novel, fully automated machine learning approach to tackle these complexities by incorporating feature selection (FS) and feature space reduction (FSR) techniques, leading to a substantial enhancement of the model’s performance. A data balancing technique is also employed during preprocessing to address data imbalance issue that is commonly encountered in clinical contexts. Finally, for reliable CKD classification, an ensemble characteristics-based classifier is encouraged. The effectiveness of our approach is rigorously validated and assessed on multiple datasets, and the clinical relevancy of the strategy is evaluated on the real-world therapeutic data collected from Bangladeshi patients. The study establishes the dominance of adaptive boosting, logistic regression, and passive aggressive ML classifiers with 96.48% accuracy in forecasting unseen therapeutic CKD data, particularly in early-stage cases. Furthermore, the effectiveness of the FSR technique in reducing the prediction time significantly is revealed. The outstanding performance of the proposed model demonstrates its effectiveness in addressing the complexity of healthcare CKD data by incorporating the FS and FSR techniques. This highlights its potential as a promising computer-aided diagnosis tool for doctors, enabling early interventions and improving patient outcomes.
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来源期刊
Applied Computational Intelligence and Soft Computing
Applied Computational Intelligence and Soft Computing COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-
CiteScore
6.10
自引率
3.40%
发文量
59
审稿时长
21 weeks
期刊介绍: Applied Computational Intelligence and Soft Computing will focus on the disciplines of computer science, engineering, and mathematics. The scope of the journal includes developing applications related to all aspects of natural and social sciences by employing the technologies of computational intelligence and soft computing. The new applications of using computational intelligence and soft computing are still in development. Although computational intelligence and soft computing are established fields, the new applications of using computational intelligence and soft computing can be regarded as an emerging field, which is the focus of this journal.
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