支持向量机
混淆矩阵
接收机工作特性
人工智能
人工神经网络
随机森林
稳健性(进化)
机器学习
计算机科学
分类器(UML)
模式识别(心理学)
生物
生物化学
基因
作者
Wanjun Zhao,Yong Zhang,Xinming Li,Yonghong Mao,Changwei Wu,Lijun Zhao,Fang Liu,Jingqiang Zhu,Jun Cheng,Hao Yang,Guisen Li
出处
期刊:Aging pathobiology and therapeutics
[Ant Publishing]
日期:2021-09-30
卷期号:3 (3): 63-72
被引量:2
标识
DOI:10.31491/apt.2021.09.064
摘要
Background: We aimed to establish a novel diagnostic model for kidney diseases by combining artificial intelligence with complete mass spectrum information from urinary proteomics. Methods: We enrolled 134 patients (IgA nephropathy, membranous nephropathy, and diabetic kidney disease) and 68 healthy participants as controls, with a total of 610,102 mass spectra from their urinary proteomic profiles. The training data set (80%) was used to create a diagnostic model using XGBoost, random forest (RF), a support vector machine (SVM), and artificial neural networks (ANNs). The diagnostic accuracy was evaluated using a confusion matrix with a test dataset (20%). We also constructed receiver operating-characteristic, Lorenz, and gain curves to evaluate the diagnostic model. Results: Compared with the RF, SVM, and ANNs, the modified XGBoost model, called Kidney Disease Classifier (KDClassifier), showed the best performance. The accuracy of the XGBoost diagnostic model was 96.03%. The area under the curve of the extreme gradient boosting (XGBoost) model was 0.952 (95% confidence interval, 0.9307–0.9733). The Kolmogorov-Smirnov (KS) value of the Lorenz curve was 0.8514. The Lorenz and gain curves showed the strong robustness of the developed model. Conclusions: The KDClassifier achieved high accuracy and robustness and thus provides a potential tool for the classification of kidney diseases
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