An Effectual Smart Interactive Approach for Handling Healthcare Data Using Bigdata Analytics

Abstract

Healthcare systems recognize and work to treat medical emergencies as omnipresent and quickly spreading observant updates of patients to doctors, vital parameter detection is becoming the most important task of the healthcare industry. It is become simpler to gather the necessary data and to accurately analyze them due to the widespread usage of smart medical gadget instruments as part of the healthcare system harnessing snipping information technologies like big data, and the Internet of Things which have the capacity to offer practical healthcare insight. In this paper, we would like to give an idea of the effectual interactive approach to handling healthcare data using distributed file systems and big data analytics. The fundamental goal of this strategy is to gather and store patient data, then using a predictive analysis strategy, process the data that has been collected. Use it to create an effective multidimensional data warehouse with disparate vital parameters of patients from various age groups so that a doctor can treat them effectively if they experience the same issue. The data analysis will also enable a doctor to determine the patient's most prevalent disease. To achieve this, we require an efficient framework that is capable of handling a huge amount of structured and unstructured patient data and live-streaming data about the patient from distinct regions. From this, the doctor will process, store, and evaluate the patient's information and go for further treatment. Apache Spark is used to process such a giant amount of data with the assistance of machine learning components supported by the spark which will play a vital role in meeting the requirements. The Hadoop node cluster, which integrates parallel computation and storage, Batch Viewing is suitable to perform the complex analysis of the immense amount of healthcare data, which is the center of our outline.