2023
DOI: 10.11591/ijece.v13i2.pp2177-2185
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Involving machine learning techniques in heart disease diagnosis: a performance analysis

Abstract: <span lang="EN-US">Artificial intelligence is a science that is growing at a tremendous speed every day and has become an essential part of many domains, including the medical domain. Therefore, countless artificial intelligence applications can be seen in the medical domain at various levels, which are employed to enhance early diagnosis and prediction and reduce the risks associated with many diseases, including heart diseases. In this article, machine learning techniques (logistic regression, random f… Show more

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Cited by 23 publications
(12 citation statements)
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“…Post-transplant infections and AKI are associated with increased healthcare costs, prolonged hospital stays, and adverse effects on both allograft and patient survival[ 116 , 119 ]. Also, ML models have been used for the diagnosis of appendicitis and heart disease[ 123 , 124 ]. Employing ML models for predicting and managing these complications holds the potential to yield improved patient outcomes, reduced healthcare expenditures, and an overall better quality of life.…”
Section: Discussionmentioning
confidence: 99%
“…Post-transplant infections and AKI are associated with increased healthcare costs, prolonged hospital stays, and adverse effects on both allograft and patient survival[ 116 , 119 ]. Also, ML models have been used for the diagnosis of appendicitis and heart disease[ 123 , 124 ]. Employing ML models for predicting and managing these complications holds the potential to yield improved patient outcomes, reduced healthcare expenditures, and an overall better quality of life.…”
Section: Discussionmentioning
confidence: 99%
“…To analyze the efficiency of XG-Boost, its accuracy is compared to that of a back propagation network [60], [61], the maximum accuracy is achieved by back propagation network. The basis of neural network training is utilized for back propagation network.…”
Section: Back Propagation Networkmentioning
confidence: 99%
“…One domain that has significantly benefited from these advancements is healthcare, where numerous applications have been proposed and implemented to facilitate medical diagnosis [15,16]. These applications have demonstrated remarkable accuracy in diagnosing a wide range of prevalent diseases, including cancer, eye diseases, heart diseases, skin lesions, gastrointestinal diseases, respiratory diseases, and diabetes [17][18][19][20][21][22][23]. The availability and widespread use of these applications have empowered medical professionals to make critical decisions for their patients with increased confidence, resulting in significant benefits for the medical field and healthcare services as a whole.…”
Section: Related Workmentioning
confidence: 99%