Healthcare Big Data Analysis with Artificial Neural Network for Cardiac Disease Prediction

Abstract

The generation of a huge volume of structured, semi-structured and unstructured real-time health monitoring data and its storage in the form of electronic health records (EHRs) need to be processed and analyzed intelligently to provide timely healthcare. A big data analytic platform is an alternative to the traditional warehouse paradigms for the processing, analysis and storage of the tremendous volume of healthcare data. However, the manual analysis of these voluminous, multi-variate patients data is tedious and error-prone. Therefore, an intelligent solution method is highly essential to perform multiple correlation analyses for disease diagnosis and prediction. In this paper, first, a structural framework is proposed to process the huge volume of cardiological big data generated from the hospital and patients. Then, an intelligent analytical model for the cardiological big data analysis is proposed by combining the concept of artificial neural network (ANN) and particle swarm optimization (PSO) to predict the abnormalities in the cardiac health of a person. In the proposed cardiac disease prediction model, an extensive electrocardiogram (ECG) data analysis method is developed to identify the probable normal and abnormal cardiac feature points. Simulation results show the effects of a number of attributes for improving the accuracy of the cardiac disease prediction and data processing time in the cloud with an increase in the number of the cardiac patients.