Review of Machine Learning System for Cardiovascular Diseases Detection and Classification Based on Big Data
Keywords:
Machine Learning, Cardiovascular, Diseases, Detection, Naïve Bayes, Random Forest and Support Vector MachineAbstract
Cardiovascular disease is one of the top causes of death throughout the world. The early detection of these diseases is necessary to save lives. Using machine learning classification algorithms in healthcare organizations produces impressive results that assist medical professionals in correctly and rapidly diagnosing diseases of these kinds. The academic community is not yet fully using the huge amounts of data that healthcare companies generate. It is possible to extract important information from datasets with the use of machine learning technologies, creating more accurate results. By the survey findings, combining the feature optimization techniques PSO and ACO with the machine learning techniques KNN and RF yields an accuracy level of 99.65% minimum. To assist healthcare practitioners in making sound decisions, it is possible that future research can concentrate on developing a sophisticated model that makes use of machine learning and optimization techniques.
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