Application of decision tree and neural network for diagnosis and prescription of pediatric foot disorders

Decision trees and neural networks are typical data classification methods in data mining methods. Decision trees are so fast and interpretable, but these can be misclassified. Neural networks are slow while more reliable algorithms. In this paper, we have studied an intelligent system that diagnose and prescribe patients with pediatric foot disorder using decision tree and neural network. The object of this study was to discover meaningful knowledge between the foot disorder and biomechanical parameters related to symptoms using C5.0 decision tree and neural network. The first medical record data of 174 pediatric patients were extracted for analysis, in total 279 records, and they were diagnosed with a complex foot disorder. The dependent variable consists of five complex disorder groups, and 14 independent variables related to disorder groups were selected by importance, in 34 variables. The extracted data was separated to generate an ideal prediction model. After development of the prediction model, the prediction rate was verified and neural networks were applied for analysis of predictor importance and classification prediction. Consequently, a major symptom information in 13 diagnosis patterns were confirmed.