In this paper, a quantum based binary neural network algorithm is proposed, named as novel quantum binary neural network algorithm (NQ-BNN). It forms a neural network structure by deciding weights and separability parameter in quantum based manner. Quantum computing concept represents solution probabilistically and gives large search space to find optimal value of required parameters using Gaussian random number generator. The neural network structure forms constructively having three number of layers input layer: hidden layer and output layer. A constructive way of deciding the network eliminates the unnecessary training of neural network. A new parameter that is a quantum separability parameter (QSP) is introduced here, which finds an optimal separability plane to classify input samples. During learning, it searches for an optimal separability plane. This parameter is taken as the threshold of neuron for learning of neural network. This algorithm is tested with three benchmark datasets and produces improved results than existing quantum inspired and other classification approaches.

The amount of data produced within health informatics has grown to be quite vast. The large volume of data generated by various vital sign monitoring devices needs to be analysed in real time to alert the care providers about changes in a patients condition. Data processing in real time has complex challenges for thelarge volume of data. The real-time system should be able to collect millions of events per seconds and handle parallel processing to extract meaningful information efficiently. In our study, we have proposed a real-time BigData and Predictive Analytical Architecture for healthcare application. The proposed architecture comprises three phases: (1) collection of data, (2) offline data management and prediction model building and (3) real-time processing and actual prediction. We have used Apache Kafka, Apache Sqoop, Hadoop, MapReduce, Storm and logistic regression to predict an emergency condition. The proposed architecture can perform early detection of emergency in real time, and can analyse structured and unstructured data like Electronic Health Record (EHR) to perform offline analysis to predict patient’s risk for disease or readmission. We have evaluated prediction performance on different benchmark datasets to detect an emergency condition of any patient in real time and possibility of readmission.