KDClassifier: A urinary proteomic spectra analysis tool based on machine learning for the classification of kidney diseases | Zhao | Aging Pathobiology and Therapeutics

KDClassifier: A urinary proteomic spectra analysis tool based on machine learning for the classification of kidney diseases

Wanjun Zhao, Yong Zhang, Xinming Li, Yonghong Mao, Changwei Wu, Lijun Zhao, Fang Liu, Jingqiang Zhu, Jingqiu Cheng, Hao Yang, Guisen Li

Abstract


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.

Conclusion: The KDClassifier achieved high accuracy and robustness and thus provides a potential tool for the classification of kidney diseases.

Keywords: Kidney disease classification, urinary proteomics, machine learning algorithm, diagnosis, artificial intelligence




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