Procedia Computer Science · 2022 · 31 citations · 12 references
ComputingCardiologyComputer ScienceArtificial IntelligenceClinical Decision SupportMachine LearningCardiac Risk AssessmentCardiovascular DiseaseCardiovascular HealthCardiovascular RiskDeep LearningRisk PredictionCardiovascular Risk DetectionComputer-aided Electrocardiogram InterpretationHealth Event PredictionHeart Rate Variability MeasurementHeart Rate Variability ModelingMachine Learning For Health MonitoringMachine Learning For HealthcareMachine Learning Prediction Models
Artificial Intelligence is exponentially evolving into a solution to many of humanity's complex problems. In this context, healthcare is benefiting from this technology and all its branches to improve the level of services offered, including cardiac health services. Cardiovascular diseases have always been among the most common and deadly diseases around the world, as studies have consistently shown. However, Artificial Intelligence services offer several tools to improve the diagnosis of these diseases and even predict their occurrence. In this study, four models are created and trained with ”PhsyioNet Smart Health for Assessing the Risk of Events via ECG Database” to analyze the characteristics of heart rate variability and predict the occurrence of heart diseases and cerebrovascular events. The results obtained support the confidence in the use of Artificial Intelligence in cardiology, where Support Vector Machines, Deep Neural Networks, and XGBoost achieved an accuracy of 91.80%, 90.19%, and 89.10%, respectively.
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Dong Xibin, Zhiwen Yu, Wenming Cao et al. · Frontiers of Computer Science · 2019 · 1.9K citations
Computing, Data Science, Data Mining +11