Classification of Pediatric Pneumonia Prediction Approaches

Pneumonia is a highly dangerous and infectious illness that affects one or both lungs. It affects 7% of the population worldwide and results in 3 million pediatric deaths annually. There is a dearth in the research involving prediction of pediatric pneumonia when compared to adult pneumonia. Machine learning methods like Convolutional Neural Networks, Multi-layer Perceptron, Recurrent Neural Networks and typical classification and regression techniques have been used for adults. In this paper, we are doing a classification and comparative analysis of all these approaches, so as to allow future researchers to apply appropriate techniques as per their needs and available resources in their pediatric pneumonia research.