چکیده مقاله
Technology has been used more and more in recent years to help avoid awide range of illnesses Heart problems are among the deadliest of them,resulting from a variety of causative variables However, the start ofcardiac disease can be predicted and prevented with the appropriateknowledge and methods In this work, we conduct a thoroughinvestigation of methods designed to categorize cardiac disease using adataset assembled in 1988 We use a variety of machine learningalgorithms, such as decision trees, naïve Bayes, logistic regression,random forests, support vector machines, extreme gradient boost, and knearestneighbors, to do this We determine the individual performancesof these algorithms and determine which ones perform best by rigorouslyevaluating them Additionally, we explore relevant debates regardingthese algorithms' operational preparedness for practical use Our goal indoing this research is to make a small but meaningful contribution to thecurrent attempts to use technology to prevent heart disease and otherpreventive healthcare initiatives Our results provide insight into thepractical factors necessary for the use of machine learning techniques inclinical settings, in addition to illuminating the effectiveness of differentapproaches in disease classification
کلیدواژهها
نویسندگان
Mahboubeh Molavi Arabshahi
School of Mathematics and Computer Science, Iran University of Science and Technology
Arman Alaei
School of Mathematics and Computer Science, Iran University of Science and Technology
شیوه ارجاع
Molavi Arabshahi, Mahboubeh and Alaei, Arman,1402,Advanced Machine Learning Approaches for Heart DiseasePrediction and Prevention with Comparative Analysis ofClassification Algorithms,The first international conference on artificial intelligence and its future prospects in electrical, computer, mechanical and telecommunication engineering sciences.,Mashhad
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مجموعه مقالات نخستین کنفرانس بین المللی هوش مصنوعی و چشم انداز آینده آن در علوم مهندسی برق ، کامپیوتر ، مکانیک و مخابرات21 اسفند 1402 · مشهد