AI Summary & Key Takeaways
AI InsightAs a result of the application, the SVM technique achieved the best accuracy outcomes as 87.91%.
📌 3 Key Takeaways
Abstract
Due to changing lifestyles in the world and in our country, the account of chronic diseases (CD) is rising day after day. CD is one of the most widespread reason of death. About 46% of the death of people in the world, excluding communicable diseases and accidents, are because of cardiovascular diseases (CVDs), according to this study, and 7.4 million of her 17.5 million deaths from these diseases are due to heart attacks. It was something. The number of deaths from cardiovascular disease is estimated to reach 22.2 million in 2030. The fact that most of the agents that are the reasons of the heart disease (HD) that can be prevented and treated is an important phenomenon in reducing cardiovascular disease deaths. Accurate and timely diagnosis of HD is therefore plenty important. Used machine learning (ML) techniques to determine heart attack risk in this study. Therefore, heart attack risk assessment was performed with a less expensive and effective approach. In this study, Logistic Regression, Support Vector Machines (SVM), Nearest Neighbor Algorithms, NaiveBayes, and Random Forest, ML techniques were applied to a data set containing 303 patient records and 14 variables. As a result of the application, the SVM technique achieved the best accuracy outcomes as 87.91%.