The Personalized Disease Predication Care from Harm using Big Data Analytics in Healthcare

Background/Objectives: Nowadays, big data plays an important role in various areas such as industries, research, education, hospitals and etc., healthcare has its vitality in medical streams. Methods/Statistical Analysis: Healthcare is a data-rich industry. Executive databases embrace an incredible number of transactions for each patient treated. Though the healthcare industry has been a meadow, this change has the probable to be revolutionarily. It provides medical solutions for the different kinds of diseases. The manually maintained records are electronically stored in the database. Findings: A specialized tool disease recommendation system is used for entering personalised model health profile of the victims. This tool stumbles on entering large number of data and health profiles. It also increases the computational time, so this function in a timeframe for clinical use. Improvements/Applications: This paper begins by analyzing the performance limitation for personalized disease prediction contraption CARE (Collaborative Assessment and Recommendation Engine). CARE is analysis in two categories, they are Current CARE architecture and Parallel CARE architecture for performance benefits on big patient data.