The role of big data in precision medicine and healthcare monitoring using the MapReduce framework

Abstract

Data analytics has become a cornerstone of precision medicine by enabling doctors and scientists to extract meaningful insights from vast, complex data sets. Most healthcare data are high-dimensional data that not only require longer computational time but also affect the accuracy of analysis. In order to overcome these issues, the MapReduce based big data healthcare monitoring framework is proposed. The proposed work comprises preprocessing, the MapReduce framework, and data classification. The preprocessing can be done using improved min-max normalization, and the big data can be handled using the improved support vector machine (SVM)-recursive feature elimination (RFE) method. Finally, the classification can be done using a deep Q-network (DQN). The performance of the proposed method is analyzed in terms of accuracy, precision, F-measure, and Matthew's correlation coefficient (MCC).