Classification weight of new born baby at Sayang Ibu maternity Hospital with probabilistic neural network (PNN)

Abstract

The purpose of this research is to classify baby weight before baby was born by considering maternal factors such as maternal weight, maternal height, maternal hemoglobin (Hb), maternal blood pressure, maternal age, total parity and fetal factors such as twin status using PNN (Probabilistic Neural Network). PNN is one of the models in Artificial Neural Networks (ANN) that exists for classification. PNN can be used in classification because it can classify with optimal results, higher accuracy and faster than other neural network models. PNN is structured by four layers, namely the input layer, pattern layer, summation layer and output layer. From classification with PNN on weight of new born baby using 7 factors, we obtained that the best proportion of accuracy classification on weight of new baby born is 90:10 with σ value is 0,2. The accuracy of classification within training data is 100% whereas on testing data is 92%. Based on classification on testing data we obtain eleven data are classified into the same as original class and one data os predicted to the other class.