Big Data Deep Learning Framework using Keras: A Case Study of Pneumonia Prediction

Big Data predictive analytics using machine learning techniques is currently a much active area of research in medical science. With increasing size and complexity of medical data like X-rays, deep learning gained huge success in prediction of many fatal diseases like pneumonia. In this research work, DCNN (deep convolutional neural networks) an efficient predicting model for big data, having deep layers is a proposed, which can classify whether a person is having a pneumonia or not. The experiments are carried after extracting the features of high quality X-ray images data and achieved an prediction accuracy of 84% and AUC of Promising results are found, when the results of the DCNN framework is compared with the regular classifiers like SVM, random forest, etc. using different evaluation metrics like accuracy, sensitivity, etc. With the appearance of increasing cases of pneumonia, tactful implementation of deep learning can play a big part in improving the performance of prediction of many fatal diseases in the future.