Optimising performance indicators in the telecommunications sector

Abstract

INTRODUCTION: This study analyses and predicts performance in the public telecommunication sector using neural networks on key performance indicators in the telecommunication sector. OBJECTIVES: Although there are several key performance indicators in the telecommunications sector, we have selected a few and assessed their correlation with the variable to be predicted. In this study, we used supervised learning based on a multi-layer neural network. METHODS: The algorithm used in this study is the retro propagation algorithm because of its simplicity and accuracy of estimation. RESULTS: The results show that the selected indicators, including accessibility, maintainability, satisfaction, network operating cost and availability, explain more than 80% of the performance in the telecommunications sector, and the area of the ROC curve is equal to 0.97, which means that the classifier is almost perfect. This is also justified by the sensitivity and specificity, which are close to 1 when observing the ROC curve and the confusion matrix. The classification error found from the confusion matrix is equal to 1%, which means that our model has very high accuracy. CONCLUSION: The other indicators presented were not selected in the model because of their low correlation with the variable of interest and the difficulty of collecting the data.