OBJECTIVETo study the feasibility and rationale of using a generalized regression neural network model integrated with multiple disease indicators for diagnosing alcoholic liver disease (ALD).

METHODSALD indicators were identified by reviewing the clinical testing results of 40 ALD patients from the literature and 135 patients from the Second Xiangya Hospital of Central South University, who were also classified by physician experts upon clinical consultation. Seven indicators were selected as diagnosis indexes and applied to a general regression neural network diagnostic model. Thirty-four of the reported patients and 120 of the clinical patients were selected for use as training samples to establish the indicator recognition pattern for the model, and the remaining six and 15 patients from the two respective groups were selected for use as testing samples to determine the model's diagnostic ability.

RESULTSThe model provided a correct diagnosis of ALD sub-classification for 94.1% (32/34) of the reported patients and 100% (120/120) of the clinical patients in the training set. The correct diagnosis rates achieved with the training sets were 100% for both the reported patient group (6/6) and the clinical patient group (15/15), indicating that the results of the diagnostic model were in good agreement with the ALD classifications generated by the clinical expert consultations.

CONCLUSIONThe general regression neural network model based on multiple indicators of ALD is capable of providing accurate and comprehensive diagnosis of ALD and may be feasible for clinical applications.