FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4285815214> ?p ?o ?g. }

• W4285815214 abstract "Heart problems are also known as cardiovascular disease, and the effect of heart disease and death rates have grown in recent decades as a result of numerous heart disease situations. Many risk factors are linked to heart disease, which necessitates taking the effort to develop reliable, accurate, and practical strategies for recognizing an initial discovery and achieving quick disease management. Heart problems are present at an early stage, indicating that the patient&#x0027;s health has to be improved. On the basis of supervised learning algorithms, this study paper gives numerous attributes related with heart disease and the model. Here, Machine Learning techniques are used to forecast the emergence of cardiac ailments at an earlier stage in order to fix the problem. A tendency is used to examine the parameters such as sex, age, and weight, as well as tests such as cholesterol, blood pressure, and diabetes, are used to predict outcomes. Many algorithms are employed to tackle this problem in machine learning. The logistic regression techniques are used to forecast the patient&#x0027;s heart disease in this paper. Patients&#x0027; health issues are also recommended by logistic regression algorithms, as well as health advice to help them improve their health. The goal of this study is to predict the progression of cardiac disease in patients. The findings show that the logistic regression technique achieves the greatest accuracy score." @default.
• W4285815214 created "2022-07-19" @default.
• W4285815214 creator A5005994271 @default.
• W4285815214 creator A5027693143 @default.
• W4285815214 creator A5041073016 @default.
• W4285815214 creator A5061280607 @default.
• W4285815214 creator A5077436007 @default.
• W4285815214 creator A5087860519 @default.
• W4285815214 date "2022-04-28" @default.
• W4285815214 modified "2023-10-18" @default.
• W4285815214 title "Forecast of Heart Sickness using Machine Learning" @default.
• W4285815214 cites W2147273498 @default.
• W4285815214 cites W2793148728 @default.
• W4285815214 cites W2999703049 @default.
• W4285815214 cites W3000246709 @default.
• W4285815214 cites W3092509330 @default.
• W4285815214 cites W3142099614 @default.
• W4285815214 cites W3157934394 @default.
• W4285815214 cites W3172467253 @default.
• W4285815214 cites W3173084966 @default.
• W4285815214 cites W3202492884 @default.
• W4285815214 cites W3204579196 @default.
• W4285815214 cites W3215511615 @default.
• W4285815214 cites W4211098578 @default.
• W4285815214 doi "https://doi.org/10.1109/icacite53722.2022.9823777" @default.
• W4285815214 hasPublicationYear "2022" @default.
• W4285815214 type Work @default.
• W4285815214 citedByCount "9" @default.
• W4285815214 countsByYear W42858152142023 @default.
• W4285815214 crossrefType "proceedings-article" @default.
• W4285815214 hasAuthorship W4285815214A5005994271 @default.
• W4285815214 hasAuthorship W4285815214A5027693143 @default.
• W4285815214 hasAuthorship W4285815214A5041073016 @default.
• W4285815214 hasAuthorship W4285815214A5061280607 @default.
• W4285815214 hasAuthorship W4285815214A5077436007 @default.
• W4285815214 hasAuthorship W4285815214A5087860519 @default.
• W4285815214 hasConcept C119857082 @default.
• W4285815214 hasConcept C126322002 @default.
• W4285815214 hasConcept C151956035 @default.
• W4285815214 hasConcept C154945302 @default.
• W4285815214 hasConcept C177713679 @default.
• W4285815214 hasConcept C2779134260 @default.
• W4285815214 hasConcept C2780074459 @default.
• W4285815214 hasConcept C41008148 @default.
• W4285815214 hasConcept C71924100 @default.
• W4285815214 hasConcept C84393581 @default.
• W4285815214 hasConceptScore W4285815214C119857082 @default.
• W4285815214 hasConceptScore W4285815214C126322002 @default.
• W4285815214 hasConceptScore W4285815214C151956035 @default.
• W4285815214 hasConceptScore W4285815214C154945302 @default.
• W4285815214 hasConceptScore W4285815214C177713679 @default.
• W4285815214 hasConceptScore W4285815214C2779134260 @default.
• W4285815214 hasConceptScore W4285815214C2780074459 @default.
• W4285815214 hasConceptScore W4285815214C41008148 @default.
• W4285815214 hasConceptScore W4285815214C71924100 @default.
• W4285815214 hasConceptScore W4285815214C84393581 @default.
• W4285815214 hasLocation W42858152141 @default.
• W4285815214 hasOpenAccess W4285815214 @default.
• W4285815214 hasPrimaryLocation W42858152141 @default.
• W4285815214 hasRelatedWork W170330 @default.
• W4285815214 hasRelatedWork W2096122 @default.
• W4285815214 hasRelatedWork W351505 @default.
• W4285815214 hasRelatedWork W4978094 @default.
• W4285815214 hasRelatedWork W5755083 @default.
• W4285815214 hasRelatedWork W5813897 @default.
• W4285815214 hasRelatedWork W6061786 @default.
• W4285815214 hasRelatedWork W7043726 @default.
• W4285815214 hasRelatedWork W8428970 @default.
• W4285815214 hasRelatedWork W8838641 @default.
• W4285815214 isParatext "false" @default.
• W4285815214 isRetracted "false" @default.
• W4285815214 workType "article" @default.