Medical Data Analytics: Techniques, Challenges, and Opportunities
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Abstract
The use of medical data analytics has evolved into an indispensable tool in the healthcare industry, with the goals of enhancing patient outcomes, lowering costs, and accelerating medical research. This article gives an overview of the many methods, problems, and possibilities that are involved in medical data analytics. The diagnosis of diseases, the search for new medications, and the treatment of patients are just some of the areas of medicine that have benefited from the use of a variety of data mining and machine learning methods, such as clustering, decision trees, and deep learning. Yet, there are still obstacles to overcome, such as issues around privacy and the interpretability of the data. Possibilities for medical data analytics include the combination of human and artificial intelligence in medical treatment as well as the incorporation of large-scale data analysis into clinical practise. Moreover, case studies on medical data analytics as well as future developments in the area are presented in this study. Overall, medical data analytics has a great deal of untapped potential to revolutionise healthcare; nevertheless, more work is required to surmount existing obstacles and fully capitalise on the opportunities it presents.
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