• Sweta Kumari Barnwal

The article offers a machine learning-based overview of the evolution of intelligent data analysis in medicine, including a historical perspective, a state-of-the-art viewpoint, and an outlook on potential future developments in this area of applied artificial intelligence. The purpose of this article is not to give a complete review, but rather to explain certain subareas and directions that, in my opinion, are critical for using machine learning in medical diagnostics. The naïve Bayesian classifier, neural networks, and decision trees are highlighted in the historical review. The author offer a comparison of several cutting-edge systems that represent each field of machine learning when applied to a variety of medical diagnostic tasks. Two case studies are used to highlight future tendencies. The first discusses a newly discovered technique for dealing with the consistency of classifier choices, which seems to have promise for intelligent data analysis in medicine. The second presents a method for utilizing machine learning to validate certain inexplicable occurrences in complementary medicine, which is not (now) recognized by the orthodox medical community but may play a significant role in total medical diagnosis and treatment in the future.

Machine Learning, Medical Diagnosis, Accuracy, Validity, Comprehensibility.

"Application of Machine Learning in Medical ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 1, page no.403-408, January-2019, Available :http://www.jetir.org/papers/JETIRFT06077.pdf

"Application of Machine Learning in Medical ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 1, page no. pp403-408, January-2019, Available at : http://www.jetir.org/papers/JETIRFT06077.pdf