{"title":"用于早期慢性肾病检测特征选择的有效角色导向二元海象灰狼法","authors":"B Mamatha, Sujatha P Terdal","doi":"10.1007/s11255-024-04067-9","DOIUrl":null,"url":null,"abstract":"<p><p>In clinical decision-making for chronic disorders like chronic kidney disease, high variability often leads to uncertainty and negative outcomes. Deep learning techniques have been developed as useful tools for minimizing the chance and improving clinical decision-making. Moreover, traditional techniques for chronic kidney disease recognition frequently the accuracy is compromised as it relies on limited sets of biological attributes. Therefore, in the proposed work, a combination of deep radial bias network and the puma optimization algorithm is suggested for precised chronic kidney disease classification. Initially, the accessed data undergo preprocessing using Spectral Z score Bag Boost K-Means SMOTE transformation, which includes robust scaling, data cleaning, balancing, encoding, handling missing values, min-max scaling, and z-standardization. Feature selection is then conducted using the hybrid methodology of Role-oriented Binary Walrus Grey Wolf Algorithm to choose discriminative features for improving classification accuracy. Then, Auto Encoder with Patch-Based Principal Component Analysis is employed for dimensionality reduction to minimize the processing time. Finally, the proposed classification method utilizes deep radial bias and the puma optimization search algorithm for effective chronic kidney disease classification. The introduced scheme is tested on two datasets: the risk factor prediction of chronic kidney disease dataset and chronic kidney disease dataset, which provides accuracies of 99.02%, and 99.15%, respectively. Experiments demonstrate that the proposed model identifies chronic kidney disease more accurately than the existing approaches.</p>","PeriodicalId":14454,"journal":{"name":"International Urology and Nephrology","volume":null,"pages":null},"PeriodicalIF":1.8000,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"An effective role-oriented binary Walrus Grey Wolf approach for feature selection in early-stage chronic kidney disease detection.\",\"authors\":\"B Mamatha, Sujatha P Terdal\",\"doi\":\"10.1007/s11255-024-04067-9\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>In clinical decision-making for chronic disorders like chronic kidney disease, high variability often leads to uncertainty and negative outcomes. Deep learning techniques have been developed as useful tools for minimizing the chance and improving clinical decision-making. Moreover, traditional techniques for chronic kidney disease recognition frequently the accuracy is compromised as it relies on limited sets of biological attributes. Therefore, in the proposed work, a combination of deep radial bias network and the puma optimization algorithm is suggested for precised chronic kidney disease classification. Initially, the accessed data undergo preprocessing using Spectral Z score Bag Boost K-Means SMOTE transformation, which includes robust scaling, data cleaning, balancing, encoding, handling missing values, min-max scaling, and z-standardization. Feature selection is then conducted using the hybrid methodology of Role-oriented Binary Walrus Grey Wolf Algorithm to choose discriminative features for improving classification accuracy. Then, Auto Encoder with Patch-Based Principal Component Analysis is employed for dimensionality reduction to minimize the processing time. Finally, the proposed classification method utilizes deep radial bias and the puma optimization search algorithm for effective chronic kidney disease classification. The introduced scheme is tested on two datasets: the risk factor prediction of chronic kidney disease dataset and chronic kidney disease dataset, which provides accuracies of 99.02%, and 99.15%, respectively. 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引用次数: 0
摘要
在慢性疾病(如慢性肾病)的临床决策中,高变异性往往会导致不确定性和负面结果。深度学习技术已被开发为有用的工具,可最大限度地减少偶然性并改善临床决策。此外,传统的慢性肾病识别技术往往依赖于有限的生物属性集,准确性大打折扣。因此,本文建议将深度径向偏置网络与 Puma 优化算法相结合,用于精确的慢性肾病分类。首先,利用光谱 Z score Bag Boost K-Means SMOTE 变换对访问的数据进行预处理,其中包括稳健缩放、数据清理、平衡、编码、处理缺失值、最小-最大缩放和 Z 标准化。然后,使用面向角色的二进制海象灰狼算法混合方法进行特征选择,以选择提高分类准确性的判别特征。然后,采用基于补丁的主成分分析的自动编码器进行降维,以尽量减少处理时间。最后,所提出的分类方法利用深度径向偏置和美洲豹优化搜索算法来有效地进行慢性肾病分类。引入的方案在两个数据集上进行了测试:慢性肾脏病风险因素预测数据集和慢性肾脏病数据集,准确率分别为 99.02% 和 99.15%。实验证明,与现有方法相比,所提出的模型能更准确地识别慢性肾病。
An effective role-oriented binary Walrus Grey Wolf approach for feature selection in early-stage chronic kidney disease detection.
In clinical decision-making for chronic disorders like chronic kidney disease, high variability often leads to uncertainty and negative outcomes. Deep learning techniques have been developed as useful tools for minimizing the chance and improving clinical decision-making. Moreover, traditional techniques for chronic kidney disease recognition frequently the accuracy is compromised as it relies on limited sets of biological attributes. Therefore, in the proposed work, a combination of deep radial bias network and the puma optimization algorithm is suggested for precised chronic kidney disease classification. Initially, the accessed data undergo preprocessing using Spectral Z score Bag Boost K-Means SMOTE transformation, which includes robust scaling, data cleaning, balancing, encoding, handling missing values, min-max scaling, and z-standardization. Feature selection is then conducted using the hybrid methodology of Role-oriented Binary Walrus Grey Wolf Algorithm to choose discriminative features for improving classification accuracy. Then, Auto Encoder with Patch-Based Principal Component Analysis is employed for dimensionality reduction to minimize the processing time. Finally, the proposed classification method utilizes deep radial bias and the puma optimization search algorithm for effective chronic kidney disease classification. The introduced scheme is tested on two datasets: the risk factor prediction of chronic kidney disease dataset and chronic kidney disease dataset, which provides accuracies of 99.02%, and 99.15%, respectively. Experiments demonstrate that the proposed model identifies chronic kidney disease more accurately than the existing approaches.
期刊介绍:
International Urology and Nephrology publishes original papers on a broad range of topics in urology, nephrology and andrology. The journal integrates papers originating from clinical practice.