This document discusses how big data analytics can help the healthcare industry by enabling earlier disease diagnosis and treatment through analysis of large amounts of structured and unstructured data from various sources. It provides examples of different types of healthcare data and describes a use case where IoT is used to remotely monitor patient vital signs. Some challenges of big data analytics in healthcare are standardized data formats, real-time analysis, privacy concerns, and ensuring results remain relevant over time. Overall, big data is transforming healthcare by powering personalized care through predictive analytics.

1. BIG DATA ANALYTICS AND
INTELLIGENCE: A
PERSPECTIVE IN
HEALTHCARE
Presented by:

2. INTRODUCTION
1. Main challenge for all organizations is to store loads of
unstructured information
2. Traditional database helps in storing only small amounts of
information
3. Hence to handle such huge amounts of structured and
unstructured information, big data is useful
4. The purpose of big data is to collect the data that is gathered from
different sources and then store this collected data in some
common place
5. This presentation aims to throw light, on how big data has
changed the healthcare industry

3. NEED FOR BIG DATA ANALYTICS IN
HEALTHCARE
i. Increasing earlier diagnosis and the effectiveness and quality of
treatments by the discovery of early signals and disease
intervention, reduced probability of adverse reactions, etc.
ii. Widening possibilities for prevention of diseases by identification
of risk factors for disease
iii. Since healthcare data is huge and unstructured, Big data can help
in efficient storage of data

4. TYPES OF HEALTHCARE DATA
1. Structured Data
2. Unstructured Data
3. Semi-Structured Data
4. Genomic data
5. Patient Behavior and Sentiment data
6. Clinical Data

6. USE CASE: SMART HEALTHCARE
USING IOT
 A robust healthcare monitoring system has been developed that is
intelligent enough to automatically monitor the vital health
parameters of patients using IoT and cloud technology
 It collects the status information of the patient’s heart rate and
temperature and sends an emergency notification to the patient’s
doctor/relative with his current status and full medical information if
there is a variation in the recorded parameters concerning the
threshold value
This would help the doctor to monitor his patient from anywhere
and also for the patient to access and send his health status directly
without visiting the hospital
We divide the whole system into different modules, such as fetch
module, ingest module, retrieve module, act module/notify module

7. FETCH MODULE

8. INGEST MODULE

9. RETRIEVE MODULE

10. NOTIFY MODULE

11. CHALLENGES
1. The healthcare data are not in a standardized format, often discovered in
fragmented form, or in some incompatible formats. So, it is suggested
that healthcare systems and data should be standardized before
proceeding for further processing
2. Real-time big data analytics is an important requirement in the healthcare
industry. To address this issue, the delay between data acquisition and
data processing should be dealt with quickly
3. Time effect is another big challenge. It may happen to occur that the
results of big data analytics may differ from time to time. The reasons
behind this may be due to change in technology or adaptation of high-
end technology
4. Concurrency in big data analytics should be maintained in an efficient way
so that data inconsistency should not occur at any instant or at any cost
as otherwise this will lead to a serious problem of affecting the whole

12. CHALLENGES
5. Adaptation of cloud technology: Emphasis should be placed on
some security models that may be implemented during clinical data
sharing or any healthcare-based data sharing over the cloud
6. Protect patient’s privacy: Patient data may be shared after de-
identification, but protecting the patient from either direct or
indirect identification while still maintaining the usefulness of the
data is challenging
7. Visualization: Big Data Analytics must produce data in most
appropriate way, so as to reduce any misleading

13. CONCLUSION
Adoption of big data technology is rapidly increasing in the healthcare
industry
The medical imaging field, is also dependent directly or partially on medical
big data since an accurate diagnosis of a serious disease at the right stage
needs continuous study
Predictive Analytics and consumer-centric healthcare systems will improve
quality of life with personalized health care
Big Data and GIS can support healthcare practitioners in smarter healthcare
strategies
A big portion of medical datasets is medical images, which are generally of
a fuzzy nature, to develop an advanced fuzzy set-based technique for
medical image data enhancement is an unavoidable aspect of the future
research. After the pre-processing step, the remaining portion of the big
data analytics will be more similar to traditional big data analytics so, future