增强慢性肾脏疾病的早期检测:一个鲁棒的机器学习模型

IF 3.7 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
M. Arif, A. Mukheimer, Daniyal Asif
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引用次数: 2

摘要

慢性疾病预后的临床决策往往受到高方差的阻碍,导致不确定性和负面结果,特别是在慢性肾脏疾病(CKD)等病例中。机器学习(ML)技术已经成为减少随机性和增强临床决策的宝贵工具。然而,传统的CKD检测方法往往缺乏准确性,因为它们依赖于有限的生物学属性集。本研究提出了一种用于预测CKD的新型ML模型,该模型结合了各种预处理步骤、特征选择、超参数优化技术和ML算法。为了解决医疗数据集中的挑战,我们采用了缺失值的迭代插值和一种新的数据缩放顺序方法,结合了鲁棒缩放、z-标准化和最小-最大缩放。使用Boruta算法进行特征选择,使用ML算法开发模型。该模型在UCI CKD数据集上进行了验证,准确率达到100%。我们的方法结合了创新的预处理步骤、Boruta特征选择和k近邻算法,以及使用网格搜索交叉验证(CV)的超参数优化,证明了其在增强CKD早期检测方面的有效性。这项研究强调了ML技术在改善临床支持系统和减少慢性疾病预后不确定性影响方面的潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Enhancing the Early Detection of Chronic Kidney Disease: A Robust Machine Learning Model
Clinical decision-making in chronic disorder prognosis is often hampered by high variance, leading to uncertainty and negative outcomes, especially in cases such as chronic kidney disease (CKD). Machine learning (ML) techniques have emerged as valuable tools for reducing randomness and enhancing clinical decision-making. However, conventional methods for CKD detection often lack accuracy due to their reliance on limited sets of biological attributes. This research proposes a novel ML model for predicting CKD, incorporating various preprocessing steps, feature selection, a hyperparameter optimization technique, and ML algorithms. To address challenges in medical datasets, we employ iterative imputation for missing values and a novel sequential approach for data scaling, combining robust scaling, z-standardization, and min-max scaling. Feature selection is performed using the Boruta algorithm, and the model is developed using ML algorithms. The proposed model was validated on the UCI CKD dataset, achieving outstanding performance with 100% accuracy. Our approach, combining innovative preprocessing steps, the Boruta feature selection, and the k-nearest neighbors algorithm, along with a hyperparameter optimization using grid-search cross-validation (CV), demonstrates its effectiveness in enhancing the early detection of CKD. This research highlights the potential of ML techniques in improving clinical support systems and reducing the impact of uncertainty in chronic disorder prognosis.
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来源期刊
Big Data and Cognitive Computing
Big Data and Cognitive Computing Business, Management and Accounting-Management Information Systems
CiteScore
7.10
自引率
8.10%
发文量
128
审稿时长
11 weeks
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