• Deepika Poojari
• Gauri Mhatre

Personalized health suggestions are becoming a useful strategy to improve patient outcomes and speed up the delivery of healthcare services. Data analytics tools, such as statistical modeling and machine learning, enable researchers and medical professionals to customize interventions and therapies for specific patients and get significant insights from a variety of healthcare data sources. This study provides an in-depth analysis of the function of data analytics in producing customized healthcare recommendations. In order to get started, we first examine the state of personalized healthcare recommendations and pertinent research on data analytics methods used in healthcare recommendation systems. We then look into the many types of data that are frequently utilized in healthcare analytics, such as data from wearables, medical imaging, and electronic health records (EHRs). We also look at preparation techniques to deal with data privacy and quality issues. We highlight in our conclusion the contribution of statistical analysis to the development of personalized medicine recommendations. We also offer insightful information for stakeholders in the sector and future research. Personalized healthcare suggestions made possible by data analytics have the power to completely transform the way healthcare is provided by lowering costs and enhancing patient outcomes.

Health Information Technology, Data Integration, Big Data in Healthcare, Health Informatics, etc.

"Data Analytics for personalized healthcare recommendations", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 6, page no.d393-d398, June-2024, Available :http://www.jetir.org/papers/JETIR2406348.pdf

"Data Analytics for personalized healthcare recommendations", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 6, page no. ppd393-d398, June-2024, Available at : http://www.jetir.org/papers/JETIR2406348.pdf