Researcher Framework Using MongoDB and FCM Clustering for Prediction of the Future of Patients from EHR

Abstract

Biomedical engineering field is one of the most important research areas for patient diagnosis and prediction of diseases using old history of various patient information. Data collection and data analysis models changed business trends over the past few years. By using BigData analytics, we can predict the effects of drugs and how drugs develop disease on mankind. Many machine learning algorithms like cluster computing environment, classification, etc., analyze the content of healthcare. The proposed framework has developed C-means clustering algorithm in developing biomedical engineering applications. The data is collected from machine learning repository. BigData framework MongoDB database is used to analyze the data. Here, we have modules like doctor, administrator, and analyst. By using machine learning and BigData, the analyst module identifies the chronic disease and help in reducing the medical expenses. Doctor by observing the predicted data symptoms predicts related disease. Administrator’s role is to add or remove the users in the database. The researcher finds the patient parameter by using fixed, alphanumerical, variable field data, or combination data which design the exact query which executed on MongoDB server and shows the search result that result has processed by using modified FCM cluster, and finally, we have calculated the accuracy on the basis of search parameter which always helps the researcher for diagnosis of the patient history.