COMPUTER-AIDED MODEL FOR THE CLASSIFICATION OF ACUTE INFLAMMATIONS VIA RADIAL-BASED FUNCTION ARTIFICIAL NEURAL NETWORK

Abstract

Abstract
 Objective: This study aimed to compare the classification performance of acute inflammation by applying the RBF ANN model on an open-access acute inflammation data set and determining the risk factors that may be associated with acute inflammation markers.
 Material and Methods: In the study, Nephritis of renal pelvis origin was classified using the open access “Acute Inflammation” data set RBF ANN model, and risk factors that could be associated were revealed. The success of RBF ANN is presented by different performance metrics.
 Results: The success of classifying Nephritis of renal pelvis origin with the RBF ANN model has been demonstrated to be excellent (AUC = 1, Accuracy = 100%). In addition, the RBF ANN model revealed that the most important variable among the risk factors that may be associated with Nephritis of renal pelvis origin is “temperature of patient”.
 Conclusion: As a result, the obtained findings show that the RBF ANN model provides very successful predictions in the classification of Nephritis of renal pelvis origin. Also, it has been shown that the importance values of factors associated with Nephritis of renal pelvis origin are estimated with the RBF classification model and can be used safely in preventive medicine applications.