Prediction Model For The Diagnosis Of Neonatal Jaundice

Prediction Model for the Diagnosis of Neonatal Jaundice Objective: Most of the existing methods used in classification are based on traditional statistics, which provides conclusion only for the situation where sample size is tending to infinity. When confronted with the problem of predicting the neonatal jaundice, most exiting methods may not work well. In this paper, Support Vector Machine, which is a new technique for data classification, was used to establish a prediction model for neonatal jaundice. In addition, the method of Probabilistic Neural Network was also applied to this issue as a contrast.Method: A prediction model based on the Support Vector Machine theory was established for this issue, in which cross-validation procedure was applied to prevent the over-fitting problem. Besides, another model based on Probabilistic Neural Network was also established for this issue. Then, the generalization error of those two models were obtained and compared.Result: The classification results of the Support Vector Machine are in good accordance with the observed values. The generalization error of Support Vector Machine is approximately equal to that of Probabilistic Neural Network. And Support Vector Machine is easier to use than Neural Networks.Conclusion: It is believed that the study of SVM is becoming a new hotarea in the field of machine learning. Using Support Vector Machinetheory to solve classification problem is a method with promisingprospect.