Question 1:

Using tasks in data mining, there are four main tasks (Classification/ Prediction/ Clustering/ Association). In general, health research, like other areas, needs all four tasks plus some hypothesis testing and visualization. However, the obvious applications are the diagnose using classification and prediction.

Question 2:

Yes, but is difficult to estimate the need, depending on funding and country. There are many levels: government agent/ thinktanks/ research center/ pharmaceutical companies.

Data science is a key for health research. Almost all of the medical equipment are based on input to output representation (e.g., MRI, X Rays, Ultra sound machines.... ) . Bioinformatics is based on data science, medical images.... Computer vision applications e.g., clustering/classification, tracking/detection etc. There are bundle of good books on data science or healthcare see e.g., https://www.angusrobertson.com.au/books/data-science-for-healthcare/p/9783030052485?gclid=EAIaIQobChMIzYLAqYiD5wIVlSQrCh2Z7wxbEAQYBCABEgL_o_D_BwE

Dear Dhirendra Nath ,

Data Science plays a pivotal role in monitoring patient's health and notifying necessary steps to be taken in order to prevent potential diseases from taking place.Data Scientists are using powerful predictive analytical tools to detect chronic diseases at an early level.

https://data-flair.training/blogs/data-science-in-healthcare/

Regards,

Shafagat

Data Science plays a key role in medical science. Machine learning, regression analysis, clustering, Neural Network are couple of conventional techniques. Image processing is used effectively in medical diagnosis. Now, AI is also playing vital role in medical field, which uses data science as background.

Definitely many scientists are working on this across the globe, but it is difficult to give overall summary.

Hey Dhirendra Nath

I am sharing a reference link that might help you to understand things better.

https://data-flair.training/blogs/data-science-in-healthcare/

In addition to the above answers regarding the uses of prediction, machine learning, image processing etc. I think Health Research also benefits from the less 'exciting' end of data science - things like data curation, data linkage, and exploratory analysis. Although it is often seen as unexciting, data cleaning and pre-processing is a very important, and time consuming, part of Data Science. Health Research benefits greatly from this as clean, tidy, reliable, well curated data make modelling complicated predictions much easier and more robust.