Abstract:

Heart disease is one of the primary causes of death in the modern world. Cardiovascular disease prediction presents a substantial challenge for clinical data analysis. Machine learning (ML) has proven to be helpful for aiding in decision-making and producing predictions from the large amount of data. produced by the medical industry. Also, we have seen recent developments in numerous lOT fields Utilising ML techniques (IoT). Few studies have examined the use of ML to anticipate cardiac disease. In this paper, we propose a novel strategy for finding key features using machine learning techniques, which will enhance the accuracy of cardiovascular disease prediction. It presents the forecasting algorithm. Using a linear model and a hybrid random forest to construct

Description:

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