Objective: Acute pancreatitis (AP) is a common inflammatory disorder that may develop into severe AP (SAP), resulting in life-threatening complications and even death. The purpose of this study was to explore two different machine learning models of multilayer perception-artificial neural network (MPL-ANN) and partial least squares-discrimination (PLS-DA) to diagnose and predict AP patients' severity.
Methods: The MPL-ANN and PLS-DA models were established using candidate markers from 15 blood routine parameters and five serum biochemical indexes of 133 mild acute pancreatitis (MAP) patients, 167 SAP (including 88 moderately SAP) patients, and 69 healthy controls (HCs). The independent parameters and combined model's diagnostic efficiency in AP severity differentiation were analyzed using the area under the receiver operating characteristic curve (AUC).
Results: The neutrophil to lymphocyte ratio (NLR) is the most useful marker in 20 parameters for screening AP patients [AUC = 0.990, 95% confidence interval (CI): 0.984-0.997, sensitivity 94.3%, specificity 98.6%]. The MPL-ANN model based on six optimal parameters exhibited better diagnostic and predict performance (AUC = 0.984, 95% CI: 0.960-1.00, sensitivity 92.7%, specificity 93.3%, accuracy 93.0%) than the PLS-DA model based on five optimal parameters (AUC = 0.912, 95% CI: 0.853-0.971, sensitivity 87.8%, specificity 84.4%, accuracy 84.8%) in discriminating MAP patients from SAP patients.
Conclusion: The results demonstrated that the MPL-ANN model based on routine blood and serum biochemical indexes provides a reliable and straightforward daily clinical practice tool to predict AP patients' severity.
Keywords: Artificial neural network; Diagnosis; Pancreatitis; Partial least squares-discrimination; Prediction.
Copyright © 2021. Published by Elsevier Inc.