Big Data

Modern Techniques and Healthcare

Big Data

Modern Techniques and Healthcare

According to the survey conducted in 2012, the total amount of health data is around 550 petabytes and the year 2020 reaches almost 26000 petabytes. Due to the heterogeneous data formats of large data sources, large volumes and associated uncertainties, raw data is becoming a trace of data. Due to this complexity, the identification of health features of medical data and the selection of class attributes for health studies require the construction techniques and tools that are very complex.

Healthcare with Big Data Analytics

Machine learning concept is same as data mining concept, where data mining data analyzes data for model identification. Unlike extraction of data based on human understanding in data mining, machine learning uses these data to understand the program. Machine learning recognizes the model of data and, as a result, changes the program's function. Electronic health record: EHR is the most comprehensive application for healthcare services. Each patient has their own medical records, including medical history, allergy diagnosis, symptoms and laboratory results. Through a secure information system, the healthcare provider includes records of patients in the public and private sectors. These files may vary; doctors may make changes over time and add new medical test results without duplicating paper work or data.

Predictive Analytics in Healthcare

In the last two years, analytical advertising has been one of the main business intelligence methods, but real applications go beyond the business environment. There are many methods for analyzing large data, including test analysis and multimedia analysis. However, one of the most important categories is analytical prediction, which includes statistical methods, such as data mining and machine studies to predict the future of current historical events. Predictive methods currently used to determine the risk of delaying a patient in hospital settings. Using this data, physicians can make important patient care decisions. Predictive analysis requires understanding and use of machine learning.

Machine Learning in Healthcare:

The concept and analysis of machine learning data is very similar, and the scanned data to identify patterns, and extracts data based on a different understanding of the human, machine learning uses the data to improve understanding of the program in the field of data mining applications. Machine learning identification data pattern, and accordingly, changes the function of the program.

Electronic Health Records:

EHR is the broadest area of health applications for high health data. Each patient has their own medical records, including medical history, allergy diagnosis, symptoms and laboratory results. Through a secure information system, the healthcare provider includes records of patients in the public and private sectors. These files may vary; doctors may make changes over time and add new medical test results without duplicating paper work or data.