A Description about the Genesis and Role of Machine Learning Techniques in the Prediction of Heart Disease
Keywords:
Artificial Intelligence, Machine Learning, Support Vector Machine, Naïve Bayes Classifier, Random Forest, Decision TreeAbstract
There is a vast impact and scope for Artificial Intelligence (AI) which is spreading in every sphere. However, major alterations were recently made by it in the medical domain, particularly in the cardiovascular community. By making a framework for its advancement, a noteworthy contribution to utilizing the large multidimensional health sector datasets was made in AI. Therefore, it can be incorporated into the medical domain as of the start of basic laboratory research to clinical application and all the way up to the delivery of healthcare. Due to the difficulty in early diagnosis of Heart Disease (HD)by medical experts, it has emerged over the past ten years as the leading cause of death worldwide. Machine learning, an area of AI, serves as a beacon of hope for medical practitioners since early cardiac disease treatment cannot be applied without accurate prediction. To highlight the AI advancements in healthcare, this study describes the genesis and functions of several Machine Learning (ML) techniques on the dataset of HD extracted as of the UCI machine repository for Cardiovascular Disease prediction. A high level of accuracy for it was exhibited by results acquired from the many methodologies that use it, which also suggests that utilizing ML for it may be a superior option.
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