Data science has significantly changed the healthcare industry by enabling more informed decision making, better diagnostics, and predictive analysis. It has helped structure large amounts of medical data and allows doctors to make more educated decisions about patient health. Data science provides the foundation for drug development in the pharmaceutical sector by discovering statistical relationships between patient attributes and compounds. When applied to medical imaging, deep learning can detect small abnormalities that help doctors develop effective treatment plans. While data science holds great potential for improving healthcare, challenges remain around standardizing data and management practices.

1. Application of Data Science
in Healthcare
Anurati Kulkarni 2MAECO 2237040

2. Big Data in Health
• Big data has been explored in many
different ways, from business to
transportation, which has made us aware
of how essential it is to daily life.
• It has changed the medical industry, and
has helped in structuring and mapping
large amounts of data to fundamentally
alter how even the most basic health
monitoring operations are carried out.

4. Educated Decision Making
• The competitive need for valuable
information in the health market is the
most significant of the many variables that
make data science essential to healthcare
today.
• Consumers may receive better-quality
healthcare with the aid of the correct data
collection procedures.
• To make educated decisions on the
health conditions of their patients,
everyone from doctors to health

5. Use In Pharma
• The foundation for drug synthesis
utilizing artificial intelligence is provided
by data science, which is its main
contribution to the pharmaceutical
sector.
• Compounds are created that address the
statistical association between the
attributes using mutation profiling and
patient metadata.

6. Diagnostics
• Applications of data science in healthcare
can facilitate and accelerate diagnosis.
• In addition to helping with early health
issue diagnosis, patient data analysis
also enables the creation of medical
heatmaps that show the demographic
trends in disease.

7. Predictive Analysis
• Data science predictive models correlate
and correlate every data point to symptoms,
routines, and diseases.
Helps to:
• Manage chronic diseases
• Monitor and analyze the demand for
pharmaceutical logistics
• Predict future patient crisis
• Deliver faster hospital data documentation

8. Imaging
• Healthcare professionals often use various
imaging techniques like X-Ray, MRI, and
CT Scan to visualize your body's internal
systems and organs.
• Deep learning and image recognition
technologies in health Data Science allow
detection of minute deformities in these
scanned images, helping doctors plan an
effective treatment strategy.

9. Conclusion
• The examples of data science in healthcare
above demonstrate how big data will soon
be the dominant force in the medical field.
• Naturally, such a large potential has
hazards, such as the lack of standardized
data and ineffective data management rules.
• Yet by establishing some norms for data
science applications in healthcare and trying
to improve the tools used for data analytics in
healthcare to make them as error-free as
possible, such difficulties can be solved.